huggingface-daily-paper-feed/feed_monthly.xml

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    <title>Hugging Face Daily Papers</title>
    <link>https://huggingface.co/papers</link>
    <description>Daily research papers curated by the Hugging Face community.</description>
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    <lastBuildDate>Fri, 01 Aug 2025 00:14:52 +0000</lastBuildDate>
    <item>
      <title>Radial Attention: O(nlog n) Sparse Attention with Energy Decay for   Long Video Generation</title>
      <link>https://arxiv.org/abs/2506.19852</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2506.19852.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 40&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2506.19852</guid>
      <pubDate>Tue, 24 Jun 2025 17:59:59 +0000</pubDate>
    </item>
    <item>
      <title>Ovis-U1 Technical Report</title>
      <link>https://arxiv.org/abs/2506.23044</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2506.23044.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 60&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2506.23044</guid>
      <pubDate>Sun, 29 Jun 2025 00:40:17 +0000</pubDate>
    </item>
    <item>
      <title>MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional   Multimodal Embeddings</title>
      <link>https://arxiv.org/abs/2506.23115</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2506.23115.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Haonan Chen, Hong Liu, Yuping Luo, Liang Wang, Nan Yang, Furu Wei, Zhicheng Dou&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 36&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2506.23115</guid>
      <pubDate>Sun, 29 Jun 2025 06:41:00 +0000</pubDate>
    </item>
    <item>
      <title>Thinking with Images for Multimodal Reasoning: Foundations, Methods, and   Future Frontiers</title>
      <link>https://arxiv.org/abs/2506.23918</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2506.23918.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 84&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2506.23918</guid>
      <pubDate>Mon, 30 Jun 2025 14:48:35 +0000</pubDate>
    </item>
    <item>
      <title>SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via   Multi-Agent Multi-Turn Reinforcement Learning</title>
      <link>https://arxiv.org/abs/2506.24119</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2506.24119.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 46&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2506.24119</guid>
      <pubDate>Mon, 30 Jun 2025 17:58:13 +0000</pubDate>
    </item>
    <item>
      <title>Calligrapher: Freestyle Text Image Customization</title>
      <link>https://arxiv.org/abs/2506.24123</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2506.24123.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yue Ma, Qingyan Bai, Hao Ouyang, Ka Leong Cheng, Qiuyu Wang, Hongyu Liu, Zichen Liu, Haofan Wang, Jingye Chen, Yujun Shen, Qifeng Chen&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 35&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2506.24123</guid>
      <pubDate>Mon, 30 Jun 2025 17:59:06 +0000</pubDate>
    </item>
    <item>
      <title>Does Math Reasoning Improve General LLM Capabilities? Understanding   Transferability of LLM Reasoning</title>
      <link>https://arxiv.org/abs/2507.00432</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.00432.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Maggie Huan, Yuetai Li, Tuney Zheng, Xiaoyu Xu, Seungone Kim, Minxin Du, Radha Poovendran, Graham Neubig, Xiang Yue&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 72&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.00432</guid>
      <pubDate>Tue, 01 Jul 2025 05:23:05 +0000</pubDate>
    </item>
    <item>
      <title>Should We Still Pretrain Encoders with Masked Language Modeling?</title>
      <link>https://arxiv.org/abs/2507.00994</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.00994.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Manuel Faysse, Duarte M. Alves, Emmanuel Malherbe, André F. T. Martins, Céline Hudelot, Pierre Colombo&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 74&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.00994</guid>
      <pubDate>Tue, 01 Jul 2025 17:45:48 +0000</pubDate>
    </item>
    <item>
      <title>SciArena: An Open Evaluation Platform for Foundation Models in   Scientific Literature Tasks</title>
      <link>https://arxiv.org/abs/2507.01001</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01001.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 43&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01001</guid>
      <pubDate>Tue, 01 Jul 2025 17:51:59 +0000</pubDate>
    </item>
    <item>
      <title>GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable   Reinforcement Learning</title>
      <link>https://arxiv.org/abs/2507.01006</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01006.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 205&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01006</guid>
      <pubDate>Tue, 01 Jul 2025 17:55:04 +0000</pubDate>
    </item>
    <item>
      <title>Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy</title>
      <link>https://arxiv.org/abs/2507.01352</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01352.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 51&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01352</guid>
      <pubDate>Wed, 02 Jul 2025 04:40:29 +0000</pubDate>
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      <title>Depth Anything at Any Condition</title>
      <link>https://arxiv.org/abs/2507.01634</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01634.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Boyuan Sun, Modi Jin, Bowen Yin, Qibin Hou&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 48&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01634</guid>
      <pubDate>Wed, 02 Jul 2025 12:05:57 +0000</pubDate>
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      <title>A Survey on Vision-Language-Action Models: An Action Tokenization   Perspective</title>
      <link>https://arxiv.org/abs/2507.01925</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01925.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 35&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01925</guid>
      <pubDate>Wed, 02 Jul 2025 17:34:52 +0000</pubDate>
    </item>
    <item>
      <title>LongAnimation: Long Animation Generation with Dynamic Global-Local   Memory</title>
      <link>https://arxiv.org/abs/2507.01945</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01945.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Nan Chen, Mengqi Huang, Yihao Meng, Zhendong Mao&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 74&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01945</guid>
      <pubDate>Wed, 02 Jul 2025 17:55:50 +0000</pubDate>
    </item>
    <item>
      <title>Kwai Keye-VL Technical Report</title>
      <link>https://arxiv.org/abs/2507.01949</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01949.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 126&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01949</guid>
      <pubDate>Wed, 02 Jul 2025 17:57:28 +0000</pubDate>
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    <item>
      <title>Test-Time Scaling with Reflective Generative Model</title>
      <link>https://arxiv.org/abs/2507.01951</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01951.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zixiao Wang, Yuxin Wang, Xiaorui Wang, Mengting Xing, Jie Gao, Jianjun Xu, Guangcan Liu, Chenhui Jin, Zhuo Wang, Shengzhuo Zhang, Hongtao Xie&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 98&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01951</guid>
      <pubDate>Wed, 02 Jul 2025 17:58:01 +0000</pubDate>
    </item>
    <item>
      <title>How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation   Models on Standard Computer Vision Tasks</title>
      <link>https://arxiv.org/abs/2507.01955</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.01955.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Rahul Ramachandran, Ali Garjani, Roman Bachmann, Andrei Atanov, Oğuzhan Fatih Kar, Amir Zamir&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 34&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.01955</guid>
      <pubDate>Wed, 02 Jul 2025 17:59:07 +0000</pubDate>
    </item>
    <item>
      <title>Energy-Based Transformers are Scalable Learners and Thinkers</title>
      <link>https://arxiv.org/abs/2507.02092</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.02092.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam, Peixuan Han, Hyeonjeong Ha, Aman Chadha, Yilun Du, Heng Ji, Jundong Li, Tariq Iqbal&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 57&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.02092</guid>
      <pubDate>Wed, 02 Jul 2025 19:17:29 +0000</pubDate>
    </item>
    <item>
      <title>Heeding the Inner Voice: Aligning ControlNet Training via Intermediate   Features Feedback</title>
      <link>https://arxiv.org/abs/2507.02321</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.02321.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Nina Konovalova, Maxim Nikolaev, Andrey Kuznetsov, Aibek Alanov&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 38&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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).&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.02321</guid>
      <pubDate>Thu, 03 Jul 2025 05:25:53 +0000</pubDate>
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    <item>
      <title>WebSailor: Navigating Super-human Reasoning for Web Agent</title>
      <link>https://arxiv.org/abs/2507.02592</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.02592.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 101&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.02592</guid>
      <pubDate>Thu, 03 Jul 2025 12:59:07 +0000</pubDate>
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      <title>LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with   TriMap Video Diffusion</title>
      <link>https://arxiv.org/abs/2507.02813</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.02813.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Fangfu Liu, Hao Li, Jiawei Chi, Hanyang Wang, Minghui Yang, Fudong Wang, Yueqi Duan&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 59&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.02813</guid>
      <pubDate>Thu, 03 Jul 2025 17:21:23 +0000</pubDate>
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    <item>
      <title>MemOS: A Memory OS for AI System</title>
      <link>https://arxiv.org/abs/2507.03724</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.03724.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 141&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.03724</guid>
      <pubDate>Fri, 04 Jul 2025 17:21:46 +0000</pubDate>
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    <item>
      <title>Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM   Fine-Tuning Data from Unstructured Documents</title>
      <link>https://arxiv.org/abs/2507.04009</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.04009.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Ziyang Miao, Qiyu Sun, Jingyuan Wang, Yuchen Gong, Yaowei Zheng, Shiqi Li, Richong Zhang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 38&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.04009</guid>
      <pubDate>Sat, 05 Jul 2025 11:38:59 +0000</pubDate>
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    <item>
      <title>How to Train Your LLM Web Agent: A Statistical Diagnosis</title>
      <link>https://arxiv.org/abs/2507.04103</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.04103.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Megh Thakkar, Thibault Le Sellier de Chezelles, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Xue Liu, Alexandre Drouin, Laurent Charlin, Alexandre Piché, Alexandre Lacoste, Massimo Caccia&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 46&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.04103</guid>
      <pubDate>Sat, 05 Jul 2025 17:12:33 +0000</pubDate>
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      <title>DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive   World Knowledge</title>
      <link>https://arxiv.org/abs/2507.04447</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.04447.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 42&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.04447</guid>
      <pubDate>Sun, 06 Jul 2025 16:14:29 +0000</pubDate>
    </item>
    <item>
      <title>4DSloMo: 4D Reconstruction for High Speed Scene with Asynchronous   Capture</title>
      <link>https://arxiv.org/abs/2507.05163</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.05163.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yutian Chen, Shi Guo, Tianshuo Yang, Lihe Ding, Xiuyuan Yu, Jinwei Gu, Tianfan Xue&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 40&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.05163</guid>
      <pubDate>Mon, 07 Jul 2025 16:18:35 +0000</pubDate>
    </item>
    <item>
      <title>Pre-Trained Policy Discriminators are General Reward Models</title>
      <link>https://arxiv.org/abs/2507.05197</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.05197.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 37&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.05197</guid>
      <pubDate>Mon, 07 Jul 2025 16:56:31 +0000</pubDate>
    </item>
    <item>
      <title>Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality,   Long Context, and Next Generation Agentic Capabilities</title>
      <link>https://arxiv.org/abs/2507.06261</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.06261.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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, Ilaï Deutel, Nam Nguyen, Adam Langley, Flip Korn, Lucia Rossazza, Alexandre Ramé, Sagar Waghmare, Helen Miller, Vaishakh Keshava, Ying Jian, Xiaofan Zhang, Raluca Ada Popa, Kedar Dhamdhere, Blaž Bratanič, Kyuyeun Kim, Terry Koo, Ferran Alet, Yi-ting Chen, Arsha Nagrani, Hannah Muckenhirn, Zhiyuan Zhang, Corbin Quick, Filip Pavetić, 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, Aäron 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, Sławek 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 Mendonça, 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 Ćurko, 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, Jörg Bornschein, Gabriela Surita, Sarah Hodkinson, Fangtao Li, Chris Hidey, Sébastien 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 Olšák, 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 Bošnjak, Phuong Dao, Min Choi, Sneha Kudugunta, Jakub Adamek, Carlos Guía, 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, Mikołaj Rybiński, 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, Adrià 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 Ilić, Steven Baker, Julia Pawar, Li Zhong, Josh Camp, Yoel Zeldes, Shravya Shetty, Anand Iyer, Vít Listík, 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 Giménez, 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 Frömmgen, Yanhua Sun, Ralph Leith, Alfonso Castaño, 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, José Leal, Zhe Shen, Han Fu, Wei Wang, Arvind Kannan, Doron Kukliansky, Sergey Yaroshenko, Svetlana Grant, Umesh Telang, David Wood, Alexandra Chronopoulou, Alexandru Ţifrea, Tao Zhou, Tony, Nguy\~ên, 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 Świetlik, 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, Michaël Sander, Lisa Anne Hendricks, Anuj Sharma, Andrew Bolt, Eszter Vértes, 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, Paweł Wesołowski, 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, Angéline 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, 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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, Ágoston 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, Eugénie 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, Niccolò 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 Mitrović, 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 Žužić, Victor Carbune, Tao Li, Myle Ott, Félix de Chaumont Quitry, David Vilar Torres, Yuri Chervonyi, Tomy Tsai, Prem Eruvbetine, Samuel Yang, Matthew Denton, Jake Walker, Slavica Andačić, 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, Rémi 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 Köppe, Amr Khalifa, Lubo Litchev, Pichi Charoenpanit, Reed Roberts, Sachin Yadav, Yasumasa Onoe, Desi Ivanov, Megha Mohabey, Vighnesh Birodkar, Nemanja Rakićević, 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, Paweł 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 Comşa, 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, Håvard 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, André 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, François-Xavier Aubet, Yu Liang, Sami Lachgar, Danny Karmon, Yong Cheng, Amelio Vázquez-Reina, Angie Chen, Zhuyun Dai, Andy Brock, Shubham Agrawal, Chenxi Pang, Peter Garst, Mariella Sanchez-Vargas, Ivor Rendulic, Aditya Ayyar, Andrija Ražnatović, 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 Põder, 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 Taïga, Kareem Mohamed, Doug Fritz, Daniel Rodriguez, Zoubin Ghahramani, Harry Askham, Lior Belenki, James Zhao, Rahul Gupta, Krzysztof Jastrzębski, 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 Bražinskas, 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 Žanić, 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 57&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.06261</guid>
      <pubDate>Mon, 07 Jul 2025 17:36:04 +0000</pubDate>
    </item>
    <item>
      <title>StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context   Modeling</title>
      <link>https://arxiv.org/abs/2507.05240</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.05240.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Meng Wei, Chenyang Wan, Xiqian Yu, Tai Wang, Yuqiang Yang, Xiaohan Mao, Chenming Zhu, Wenzhe Cai, Hanqing Wang, Yilun Chen, Xihui Liu, Jiangmiao Pang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 45&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/}.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.05240</guid>
      <pubDate>Mon, 07 Jul 2025 17:49:41 +0000</pubDate>
    </item>
    <item>
      <title>Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for   Visual Reasoning</title>
      <link>https://arxiv.org/abs/2507.05255</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.05255.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 68&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.05255</guid>
      <pubDate>Mon, 07 Jul 2025 17:59:03 +0000</pubDate>
    </item>
    <item>
      <title>SingLoRA: Low Rank Adaptation Using a Single Matrix</title>
      <link>https://arxiv.org/abs/2507.05566</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.05566.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; David Bensaïd, Noam Rotstein, Roy Velich, Daniel Bensaïd, Ron Kimmel&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 108&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.05566</guid>
      <pubDate>Tue, 08 Jul 2025 01:11:30 +0000</pubDate>
    </item>
    <item>
      <title>T-LoRA: Single Image Diffusion Model Customization Without Overfitting</title>
      <link>https://arxiv.org/abs/2507.05964</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.05964.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Vera Soboleva, Aibek Alanov, Andrey Kuznetsov, Konstantin Sobolev&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 113&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.05964</guid>
      <pubDate>Tue, 08 Jul 2025 13:14:10 +0000</pubDate>
    </item>
    <item>
      <title>OmniPart: Part-Aware 3D Generation with Semantic Decoupling and   Structural Cohesion</title>
      <link>https://arxiv.org/abs/2507.06165</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.06165.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yunhan Yang, Yufan Zhou, Yuan-Chen Guo, Zi-Xin Zou, Yukun Huang, Ying-Tian Liu, Hao Xu, Ding Liang, Yan-Pei Cao, Xihui Liu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 54&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.06165</guid>
      <pubDate>Tue, 08 Jul 2025 16:46:15 +0000</pubDate>
    </item>
    <item>
      <title>Skywork-R1V3 Technical Report</title>
      <link>https://arxiv.org/abs/2507.06167</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.06167.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Wei Shen, Jiangbo Pei, Yi Peng, Xuchen Song, Yang Liu, Jian Peng, Haofeng Sun, Yunzhuo Hao, Peiyu Wang, Yahui Zhou&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 66&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.06167</guid>
      <pubDate>Tue, 08 Jul 2025 16:47:16 +0000</pubDate>
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      <title>CriticLean: Critic-Guided Reinforcement Learning for Mathematical   Formalization</title>
      <link>https://arxiv.org/abs/2507.06181</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.06181.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 39&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.06181</guid>
      <pubDate>Tue, 08 Jul 2025 17:03:39 +0000</pubDate>
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      <title>A Survey on Latent Reasoning</title>
      <link>https://arxiv.org/abs/2507.06203</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.06203.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 85&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.06203</guid>
      <pubDate>Tue, 08 Jul 2025 17:29:07 +0000</pubDate>
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      <title>Perception-Aware Policy Optimization for Multimodal Reasoning</title>
      <link>https://arxiv.org/abs/2507.06448</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.06448.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zhenhailong Wang, Xuehang Guo, Sofia Stoica, Haiyang Xu, Hongru Wang, Hyeonjeong Ha, Xiusi Chen, Yangyi Chen, Ming Yan, Fei Huang, Heng Ji&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 44&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.06448</guid>
      <pubDate>Tue, 08 Jul 2025 23:22:34 +0000</pubDate>
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      <title>LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+   FPS</title>
      <link>https://arxiv.org/abs/2507.07136</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07136.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Wanhua Li, Yujie Zhao, Minghan Qin, Yang Liu, Yuanhao Cai, Chuang Gan, Hanspeter Pfister&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 35&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07136</guid>
      <pubDate>Wed, 09 Jul 2025 00:19:58 +0000</pubDate>
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      <title>Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data</title>
      <link>https://arxiv.org/abs/2507.07095</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07095.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Ke Fan, Shunlin Lu, Minyue Dai, Runyi Yu, Lixing Xiao, Zhiyang Dou, Junting Dong, Lizhuang Ma, Jingbo Wang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 53&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07095</guid>
      <pubDate>Wed, 09 Jul 2025 17:52:04 +0000</pubDate>
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      <title>Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation   from Diffusion Models</title>
      <link>https://arxiv.org/abs/2507.07104</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07104.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Tiezheng Zhang, Yitong Li, Yu-cheng Chou, Jieneng Chen, Alan Yuille, Chen Wei, Junfei Xiao&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 44&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07104</guid>
      <pubDate>Wed, 09 Jul 2025 17:59:04 +0000</pubDate>
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      <title>4KAgent: Agentic Any Image to 4K Super-Resolution</title>
      <link>https://arxiv.org/abs/2507.07105</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07105.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 96&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07105</guid>
      <pubDate>Wed, 09 Jul 2025 17:59:19 +0000</pubDate>
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      <title>MIRIX: Multi-Agent Memory System for LLM-Based Agents</title>
      <link>https://arxiv.org/abs/2507.07957</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07957.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yu Wang, Xi Chen&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 60&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07957</guid>
      <pubDate>Thu, 10 Jul 2025 17:40:11 +0000</pubDate>
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    <item>
      <title>Scaling RL to Long Videos</title>
      <link>https://arxiv.org/abs/2507.07966</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07966.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 151&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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).&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07966</guid>
      <pubDate>Thu, 10 Jul 2025 17:47:40 +0000</pubDate>
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      <title>OST-Bench: Evaluating the Capabilities of MLLMs in Online   Spatio-temporal Scene Understanding</title>
      <link>https://arxiv.org/abs/2507.07984</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07984.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; JingLi Lin, Chenming Zhu, Runsen Xu, Xiaohan Mao, Xihui Liu, Tai Wang, Jiangmiao Pang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 39&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07984</guid>
      <pubDate>Thu, 10 Jul 2025 17:56:07 +0000</pubDate>
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      <title>Multi-Granular Spatio-Temporal Token Merging for Training-Free   Acceleration of Video LLMs</title>
      <link>https://arxiv.org/abs/2507.07990</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07990.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Jeongseok Hyun, Sukjun Hwang, Su Ho Han, Taeoh Kim, Inwoong Lee, Dongyoon Wee, Joon-Young Lee, Seon Joo Kim, Minho Shim&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 44&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07990</guid>
      <pubDate>Thu, 10 Jul 2025 17:59:02 +0000</pubDate>
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      <title>Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and   Methodology</title>
      <link>https://arxiv.org/abs/2507.07999</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.07999.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Haochen Wang, Xiangtai Li, Zilong Huang, Anran Wang, Jiacong Wang, Tao Zhang, Jiani Zheng, Sule Bai, Zijian Kang, Jiashi Feng, Zhuochen Wang, Zhaoxiang Zhang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 46&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.07999</guid>
      <pubDate>Thu, 10 Jul 2025 17:59:58 +0000</pubDate>
    </item>
    <item>
      <title>Upsample What Matters: Region-Adaptive Latent Sampling for Accelerated   Diffusion Transformers</title>
      <link>https://arxiv.org/abs/2507.08422</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.08422.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Wongi Jeong, Kyungryeol Lee, Hoigi Seo, Se Young Chun&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 35&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.08422</guid>
      <pubDate>Fri, 11 Jul 2025 09:07:43 +0000</pubDate>
    </item>
    <item>
      <title>Vision Foundation Models as Effective Visual Tokenizers for   Autoregressive Image Generation</title>
      <link>https://arxiv.org/abs/2507.08441</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.08441.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Anlin Zheng, Xin Wen, Xuanyang Zhang, Chuofan Ma, Tiancai Wang, Gang Yu, Xiangyu Zhang, Xiaojuan Qi&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 59&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.08441</guid>
      <pubDate>Fri, 11 Jul 2025 09:32:45 +0000</pubDate>
    </item>
    <item>
      <title>CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive   Neural Rendering</title>
      <link>https://arxiv.org/abs/2507.08776</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.08776.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zhengqing Wang, Yuefan Wu, Jiacheng Chen, Fuyang Zhang, Yasutaka Furukawa&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 53&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.08776</guid>
      <pubDate>Fri, 11 Jul 2025 17:38:52 +0000</pubDate>
    </item>
    <item>
      <title>KV Cache Steering for Inducing Reasoning in Small Language Models</title>
      <link>https://arxiv.org/abs/2507.08799</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.08799.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Max Belitsky, Dawid J. Kopiczko, Michael Dorkenwald, M. Jehanzeb Mirza, Cees G. M. Snoek, Yuki M. Asano&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 39&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.08799</guid>
      <pubDate>Fri, 11 Jul 2025 17:59:36 +0000</pubDate>
    </item>
    <item>
      <title>NeuralOS: Towards Simulating Operating Systems via Neural Generative   Models</title>
      <link>https://arxiv.org/abs/2507.08800</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.08800.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 75&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.08800</guid>
      <pubDate>Fri, 11 Jul 2025 17:59:40 +0000</pubDate>
    </item>
    <item>
      <title>Scaling Laws for Optimal Data Mixtures</title>
      <link>https://arxiv.org/abs/2507.09404</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.09404.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Mustafa Shukor, Louis Bethune, Dan Busbridge, David Grangier, Enrico Fini, Alaaeldin El-Nouby, Pierre Ablin&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 33&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.09404</guid>
      <pubDate>Sat, 12 Jul 2025 21:16:08 +0000</pubDate>
    </item>
    <item>
      <title>Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning   Systems in LLMs</title>
      <link>https://arxiv.org/abs/2507.09477</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.09477.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 76&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.09477</guid>
      <pubDate>Sun, 13 Jul 2025 03:29:41 +0000</pubDate>
    </item>
    <item>
      <title>SpeakerVid-5M: A Large-Scale High-Quality Dataset for Audio-Visual   Dyadic Interactive Human Generation</title>
      <link>https://arxiv.org/abs/2507.09862</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.09862.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Youliang Zhang, Zhaoyang Li, Duomin Wang, Jiahe Zhang, Deyu Zhou, Zixin Yin, Xili Dai, Gang Yu, Xiu Li&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 48&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.09862</guid>
      <pubDate>Mon, 14 Jul 2025 02:22:47 +0000</pubDate>
    </item>
    <item>
      <title>Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive   Token-Level Computation</title>
      <link>https://arxiv.org/abs/2507.10524</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.10524.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, Se-Young Yun&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 64&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.10524</guid>
      <pubDate>Mon, 14 Jul 2025 17:49:00 +0000</pubDate>
    </item>
    <item>
      <title>Reasoning or Memorization? Unreliable Results of Reinforcement Learning   Due to Data Contamination</title>
      <link>https://arxiv.org/abs/2507.10532</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.10532.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Mingqi Wu, Zhihao Zhang, Qiaole Dong, Zhiheng Xi, Jun Zhao, Senjie Jin, Xiaoran Fan, Yuhao Zhou, Yanwei Fu, Qin Liu, Songyang Zhang, Qi Zhang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 80&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.10532</guid>
      <pubDate>Mon, 14 Jul 2025 17:55:15 +0000</pubDate>
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    <item>
      <title>EmbRACE-3K: Embodied Reasoning and Action in Complex Environments</title>
      <link>https://arxiv.org/abs/2507.10548</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.10548.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Mingxian Lin, Wei Huang, Yitang Li, Chengjie Jiang, Kui Wu, Fangwei Zhong, Shengju Qian, Xin Wang, Xiaojuan Qi&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 33&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.10548</guid>
      <pubDate>Mon, 14 Jul 2025 17:59:46 +0000</pubDate>
    </item>
    <item>
      <title>Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with   Regularized Score Distillation Sampling</title>
      <link>https://arxiv.org/abs/2507.11061</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.11061.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Hayeon Kim, Ji Ha Jang, Se Young Chun&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 37&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.11061</guid>
      <pubDate>Tue, 15 Jul 2025 07:54:11 +0000</pubDate>
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    <item>
      <title>The Devil behind the mask: An emergent safety vulnerability of Diffusion   LLMs</title>
      <link>https://arxiv.org/abs/2507.11097</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.11097.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 62&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.11097</guid>
      <pubDate>Tue, 15 Jul 2025 08:44:46 +0000</pubDate>
    </item>
    <item>
      <title>EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and   Reasoning Modes</title>
      <link>https://arxiv.org/abs/2507.11407</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.11407.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 51&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.11407</guid>
      <pubDate>Tue, 15 Jul 2025 15:24:51 +0000</pubDate>
    </item>
    <item>
      <title>DrafterBench: Benchmarking Large Language Models for Tasks Automation in   Civil Engineering</title>
      <link>https://arxiv.org/abs/2507.11527</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.11527.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yinsheng Li, Zhen Dong, Yi Shao&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 31&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.11527</guid>
      <pubDate>Tue, 15 Jul 2025 17:56:04 +0000</pubDate>
    </item>
    <item>
      <title>RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA   Optimization</title>
      <link>https://arxiv.org/abs/2507.12142</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.12142.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Vladimir Bogachev, Vladimir Aletov, Alexander Molozhavenko, Denis Bobkov, Vera Soboleva, Aibek Alanov, Maxim Rakhuba&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 35&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.12142</guid>
      <pubDate>Wed, 16 Jul 2025 11:17:12 +0000</pubDate>
    </item>
    <item>
      <title>SWE-Perf: Can Language Models Optimize Code Performance on Real-World   Repositories?</title>
      <link>https://arxiv.org/abs/2507.12415</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.12415.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Xinyi He, Qian Liu, Mingzhe Du, Lin Yan, Zhijie Fan, Yiming Huang, Zejian Yuan, Zejun Ma&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 40&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.12415</guid>
      <pubDate>Wed, 16 Jul 2025 17:05:17 +0000</pubDate>
    </item>
    <item>
      <title>PhysX: Physical-Grounded 3D Asset Generation</title>
      <link>https://arxiv.org/abs/2507.12465</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.12465.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Ziang Cao, Zhaoxi Chen, Linag Pan, Ziwei Liu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 41&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.12465</guid>
      <pubDate>Wed, 16 Jul 2025 17:59:35 +0000</pubDate>
    </item>
    <item>
      <title>AnyCap Project: A Unified Framework, Dataset, and Benchmark for   Controllable Omni-modal Captioning</title>
      <link>https://arxiv.org/abs/2507.12841</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.12841.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yiming Ren, Zhiqiang Lin, Yu Li, Gao Meng, Weiyun Wang, Junjie Wang, Zicheng Lin, Jifeng Dai, Yujiu Yang, Wenhai Wang, Ruihang Chu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 39&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.12841</guid>
      <pubDate>Thu, 17 Jul 2025 07:04:05 +0000</pubDate>
    </item>
    <item>
      <title>The Imitation Game: Turing Machine Imitator is Length Generalizable   Reasoner</title>
      <link>https://arxiv.org/abs/2507.13332</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13332.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zhouqi Hua, Wenwei Zhang, Chengqi Lyu, Yuzhe Gu, Songyang Gao, Kuikun Liu, Kai Chen&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 47&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13332</guid>
      <pubDate>Thu, 17 Jul 2025 17:50:07 +0000</pubDate>
    </item>
    <item>
      <title>A Survey of Context Engineering for Large Language Models</title>
      <link>https://arxiv.org/abs/2507.13334</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13334.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 221&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13334</guid>
      <pubDate>Thu, 17 Jul 2025 17:50:36 +0000</pubDate>
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    <item>
      <title>Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos   with Spatio-Temporal Diffusion Models</title>
      <link>https://arxiv.org/abs/2507.13344</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13344.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yudong Jin, Sida Peng, Xuan Wang, Tao Xie, Zhen Xu, Yifan Yang, Yujun Shen, Hujun Bao, Xiaowei Zhou&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 52&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/ .&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13344</guid>
      <pubDate>Thu, 17 Jul 2025 17:59:17 +0000</pubDate>
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    <item>
      <title>π^3: Scalable Permutation-Equivariant Visual Geometry Learning</title>
      <link>https://arxiv.org/abs/2507.13347</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13347.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yifan Wang, Jianjun Zhou, Haoyi Zhu, Wenzheng Chang, Yang Zhou, Zizun Li, Junyi Chen, Jiangmiao Pang, Chunhua Shen, Tong He&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 62&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13347</guid>
      <pubDate>Thu, 17 Jul 2025 17:59:53 +0000</pubDate>
    </item>
    <item>
      <title>VisionThink: Smart and Efficient Vision Language Model via Reinforcement   Learning</title>
      <link>https://arxiv.org/abs/2507.13348</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13348.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Senqiao Yang, Junyi Li, Xin Lai, Bei Yu, Hengshuang Zhao, Jiaya Jia&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 71&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13348</guid>
      <pubDate>Thu, 17 Jul 2025 17:59:55 +0000</pubDate>
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    <item>
      <title>nablaNABLA: Neighborhood Adaptive Block-Level Attention</title>
      <link>https://arxiv.org/abs/2507.13546</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13546.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Dmitrii Mikhailov, Aleksey Letunovskiy, Maria Kovaleva, Vladimir Arkhipkin, Vladimir Korviakov, Vladimir Polovnikov, Viacheslav Vasilev, Evelina Sidorova, Denis Dimitrov&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 117&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13546</guid>
      <pubDate>Thu, 17 Jul 2025 21:36:36 +0000</pubDate>
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    <item>
      <title>A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges   in Russian Speech Generative Models</title>
      <link>https://arxiv.org/abs/2507.13563</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.13563.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Kirill Borodin, Nikita Vasiliev, Vasiliy Kudryavtsev, Maxim Maslov, Mikhail Gorodnichev, Oleg Rogov, Grach Mkrtchian&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 50&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.13563</guid>
      <pubDate>Thu, 17 Jul 2025 22:41:40 +0000</pubDate>
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      <title>NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining</title>
      <link>https://arxiv.org/abs/2507.14119</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.14119.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Maksim Kuprashevich, Grigorii Alekseenko, Irina Tolstykh, Georgii Fedorov, Bulat Suleimanov, Vladimir Dokholyan, Aleksandr Gordeev&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 53&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.14119</guid>
      <pubDate>Fri, 18 Jul 2025 17:50:00 +0000</pubDate>
    </item>
    <item>
      <title>Franca: Nested Matryoshka Clustering for Scalable Visual Representation   Learning</title>
      <link>https://arxiv.org/abs/2507.14137</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.14137.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Shashanka Venkataramanan, Valentinos Pariza, Mohammadreza Salehi, Lukas Knobel, Spyros Gidaris, Elias Ramzi, Andrei Bursuc, Yuki M. Asano&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 31&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.14137</guid>
      <pubDate>Fri, 18 Jul 2025 17:59:55 +0000</pubDate>
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      <title>MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via   Context-Aware Multi-Stage Policy Optimization</title>
      <link>https://arxiv.org/abs/2507.14683</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.14683.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 117&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.14683</guid>
      <pubDate>Sat, 19 Jul 2025 16:21:23 +0000</pubDate>
    </item>
    <item>
      <title>The Invisible Leash: Why RLVR May Not Escape Its Origin</title>
      <link>https://arxiv.org/abs/2507.14843</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.14843.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Fang Wu, Weihao Xuan, Ximing Lu, Zaid Harchaoui, Yejin Choi&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 81&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.14843</guid>
      <pubDate>Sun, 20 Jul 2025 07:04:08 +0000</pubDate>
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      <title>MUR: Momentum Uncertainty guided Reasoning for Large Language Models</title>
      <link>https://arxiv.org/abs/2507.14958</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.14958.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Luu Anh Tuan, Haiteng Zhao, Qika Lin, Jun Liu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 45&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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%.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.14958</guid>
      <pubDate>Sun, 20 Jul 2025 13:36:19 +0000</pubDate>
    </item>
    <item>
      <title>WebShaper: Agentically Data Synthesizing via Information-Seeking   Formalization</title>
      <link>https://arxiv.org/abs/2507.15061</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.15061.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 44&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.15061</guid>
      <pubDate>Sun, 20 Jul 2025 17:53:37 +0000</pubDate>
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    <item>
      <title>GR-3 Technical Report</title>
      <link>https://arxiv.org/abs/2507.15493</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.15493.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 44&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.15493</guid>
      <pubDate>Mon, 21 Jul 2025 10:54:13 +0000</pubDate>
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    <item>
      <title>GUI-G^2: Gaussian Reward Modeling for GUI Grounding</title>
      <link>https://arxiv.org/abs/2507.15846</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.15846.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Fei Tang, Zhangxuan Gu, Zhengxi Lu, Xuyang Liu, Shuheng Shen, Changhua Meng, Wen Wang, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, Yueting Zhuang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 126&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.15846</guid>
      <pubDate>Mon, 21 Jul 2025 17:53:42 +0000</pubDate>
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    <item>
      <title>SeC: Advancing Complex Video Object Segmentation via Progressive Concept   Construction</title>
      <link>https://arxiv.org/abs/2507.15852</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.15852.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong, Songxin He, Jianfan Lin, Junsong Tang, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 37&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.15852</guid>
      <pubDate>Mon, 21 Jul 2025 17:59:02 +0000</pubDate>
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    <item>
      <title>Deep Researcher with Test-Time Diffusion</title>
      <link>https://arxiv.org/abs/2507.16075</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16075.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Rujun Han, Yanfei Chen, Zoey CuiZhu, Lesly Miculicich, Guan Sun, Yuanjun Bi, Weiming Wen, Hui Wan, Chunfeng Wen, Solène Maître, George Lee, Vishy Tirumalashetty, Emily Xue, Zizhao Zhang, Salem Haykal, Burak Gokturk, Tomas Pfister, Chen-Yu Lee&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 51&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16075</guid>
      <pubDate>Mon, 21 Jul 2025 21:23:21 +0000</pubDate>
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    <item>
      <title>Pixels, Patterns, but No Poetry: To See The World like Humans</title>
      <link>https://arxiv.org/abs/2507.16863</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16863.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 62&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16863</guid>
      <pubDate>Mon, 21 Jul 2025 21:50:16 +0000</pubDate>
    </item>
    <item>
      <title>Step-Audio 2 Technical Report</title>
      <link>https://arxiv.org/abs/2507.16632</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16632.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 59&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16632</guid>
      <pubDate>Tue, 22 Jul 2025 14:23:55 +0000</pubDate>
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    <item>
      <title>Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning</title>
      <link>https://arxiv.org/abs/2507.16746</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16746.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 32&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16746</guid>
      <pubDate>Tue, 22 Jul 2025 16:35:36 +0000</pubDate>
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    <item>
      <title>Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning</title>
      <link>https://arxiv.org/abs/2507.16784</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16784.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Hongyin Luo, Nathaniel Morgan, Tina Li, Derek Zhao, Ai Vy Ngo, Philip Schroeder, Lijie Yang, Assaf Ben-Kish, Jack O'Brien, James Glass&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 112&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16784</guid>
      <pubDate>Tue, 22 Jul 2025 17:30:04 +0000</pubDate>
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    <item>
      <title>MegaScience: Pushing the Frontiers of Post-Training Datasets for Science   Reasoning</title>
      <link>https://arxiv.org/abs/2507.16812</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16812.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Run-Ze Fan, Zengzhi Wang, Pengfei Liu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 50&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16812</guid>
      <pubDate>Tue, 22 Jul 2025 17:59:03 +0000</pubDate>
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      <title>ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent   Planning</title>
      <link>https://arxiv.org/abs/2507.16815</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16815.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Chi-Pin Huang, Yueh-Hua Wu, Min-Hung Chen, Yu-Chiang Frank Wang, Fu-En Yang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 35&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.16815</guid>
      <pubDate>Tue, 22 Jul 2025 17:59:46 +0000</pubDate>
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    <item>
      <title>DesignLab: Designing Slides Through Iterative Detection and Correction</title>
      <link>https://arxiv.org/abs/2507.17202</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.17202.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Jooyeol Yun, Heng Wang, Yotaro Shimose, Jaegul Choo, Shingo Takamatsu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 47&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.17202</guid>
      <pubDate>Wed, 23 Jul 2025 04:49:48 +0000</pubDate>
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    <item>
      <title>Can One Domain Help Others? A Data-Centric Study on Multi-Domain   Reasoning via Reinforcement Learning</title>
      <link>https://arxiv.org/abs/2507.17512</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.17512.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yu Li, Zhuoshi Pan, Honglin Lin, Mengyuan Sun, Conghui He, Lijun Wu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 33&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.17512</guid>
      <pubDate>Wed, 23 Jul 2025 13:51:04 +0000</pubDate>
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    <item>
      <title>Yume: An Interactive World Generation Model</title>
      <link>https://arxiv.org/abs/2507.17744</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.17744.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Xiaofeng Mao, Shaoheng Lin, Zhen Li, Chuanhao Li, Wenshuo Peng, Tong He, Jiangmiao Pang, Mingmin Chi, Yu Qiao, Kaipeng Zhang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 77&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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/.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.17744</guid>
      <pubDate>Wed, 23 Jul 2025 17:57:09 +0000</pubDate>
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    <item>
      <title>Group Sequence Policy Optimization</title>
      <link>https://arxiv.org/abs/2507.18071</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.18071.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 248&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.18071</guid>
      <pubDate>Thu, 24 Jul 2025 03:50:32 +0000</pubDate>
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    <item>
      <title>The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane   Algorithm</title>
      <link>https://arxiv.org/abs/2507.18553</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.18553.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Jiale Chen, Torsten Hoefler, Dan Alistarh&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 33&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.18553</guid>
      <pubDate>Thu, 24 Jul 2025 16:22:18 +0000</pubDate>
    </item>
    <item>
      <title>Captain Cinema: Towards Short Movie Generation</title>
      <link>https://arxiv.org/abs/2507.18634</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.18634.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Junfei Xiao, Ceyuan Yang, Lvmin Zhang, Shengqu Cai, Yang Zhao, Yuwei Guo, Gordon Wetzstein, Maneesh Agrawala, Alan Yuille, Lu Jiang&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 37&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.18634</guid>
      <pubDate>Thu, 24 Jul 2025 17:59:56 +0000</pubDate>
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    <item>
      <title>Agentic Reinforced Policy Optimization</title>
      <link>https://arxiv.org/abs/2507.19849</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.19849.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 108&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.19849</guid>
      <pubDate>Sat, 26 Jul 2025 07:53:11 +0000</pubDate>
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    <item>
      <title>ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World   Shorts</title>
      <link>https://arxiv.org/abs/2507.20939</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.20939.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 55&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.20939</guid>
      <pubDate>Mon, 28 Jul 2025 15:52:36 +0000</pubDate>
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    <item>
      <title>SmallThinker: A Family of Efficient Large Language Models Natively   Trained for Local Deployment</title>
      <link>https://arxiv.org/abs/2507.20984</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.20984.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 41&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.20984</guid>
      <pubDate>Mon, 28 Jul 2025 16:45:14 +0000</pubDate>
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      <title>Reconstructing 4D Spatial Intelligence: A Survey</title>
      <link>https://arxiv.org/abs/2507.21045</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.21045.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yukang Cao, Jiahao Lu, Zhisheng Huang, Zhuowei Shen, Chengfeng Zhao, Fangzhou Hong, Zhaoxi Chen, Xin Li, Wenping Wang, Yuan Liu, Ziwei Liu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 31&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.21045</guid>
      <pubDate>Mon, 28 Jul 2025 17:59:02 +0000</pubDate>
    </item>
    <item>
      <title>A Survey of Self-Evolving Agents: On Path to Artificial Super   Intelligence</title>
      <link>https://arxiv.org/abs/2507.21046</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.21046.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 65&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.21046</guid>
      <pubDate>Mon, 28 Jul 2025 17:59:05 +0000</pubDate>
    </item>
    <item>
      <title>Rep-MTL: Unleashing the Power of Representation-level Task Saliency for   Multi-Task Learning</title>
      <link>https://arxiv.org/abs/2507.21049</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.21049.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zedong Wang, Siyuan Li, Dan Xu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 38&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.21049</guid>
      <pubDate>Mon, 28 Jul 2025 17:59:28 +0000</pubDate>
    </item>
    <item>
      <title>BANG: Dividing 3D Assets via Generative Exploded Dynamics</title>
      <link>https://arxiv.org/abs/2507.21493</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.21493.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Longwen Zhang, Qixuan Zhang, Haoran Jiang, Yinuo Bai, Wei Yang, Lan Xu, Jingyi Yu&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 44&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.21493</guid>
      <pubDate>Tue, 29 Jul 2025 04:21:21 +0000</pubDate>
    </item>
    <item>
      <title>HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D   Worlds from Words or Pixels</title>
      <link>https://arxiv.org/abs/2507.21809</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.21809.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 83&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.21809</guid>
      <pubDate>Tue, 29 Jul 2025 13:43:35 +0000</pubDate>
    </item>
    <item>
      <title>X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image   Generative Models Great Again</title>
      <link>https://arxiv.org/abs/2507.22058</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.22058.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 31&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.22058</guid>
      <pubDate>Tue, 29 Jul 2025 17:59:04 +0000</pubDate>
    </item>
    <item>
      <title>Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency   and Performance</title>
      <link>https://arxiv.org/abs/2507.22448</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.22448.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; 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&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 42&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.22448</guid>
      <pubDate>Wed, 30 Jul 2025 07:55:33 +0000</pubDate>
    </item>
    <item>
      <title>ScreenCoder: Advancing Visual-to-Code Generation for Front-End   Automation via Modular Multimodal Agents</title>
      <link>https://arxiv.org/abs/2507.22827</link>
      <description>&lt;p&gt;&lt;img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.22827.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yilei Jiang, Yaozhi Zheng, Yuxuan Wan, Jiaming Han, Qunzhong Wang, Michael R. Lyu, Xiangyu Yue&lt;/p&gt;&lt;p&gt;&lt;b&gt;Upvotes:&lt;/b&gt; 61&lt;/p&gt;&lt;p&gt;&lt;b&gt;Summary:&lt;/b&gt; 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.&lt;/p&gt;</description>
      <guid isPermaLink="false">https://arxiv.org/abs/2507.22827</guid>
      <pubDate>Wed, 30 Jul 2025 16:41:21 +0000</pubDate>
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