748 lines
227 KiB
XML
748 lines
227 KiB
XML
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<channel>
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<title>Hugging Face Daily Papers</title>
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<link>https://huggingface.co/papers</link>
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<description>Daily research papers curated by the Hugging Face community.</description>
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<generator>python-feedgen</generator>
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<lastBuildDate>Mon, 09 Feb 2026 00:17:23 +0000</lastBuildDate>
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<item>
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<title>DenseGRPO: From Sparse to Dense Reward for Flow Matching Model Alignment</title>
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<link>https://arxiv.org/abs/2601.20218</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.20218.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haoyou Deng, Keyu Yan, Chaojie Mao, Xiang Wang, Yu Liu, Changxin Gao, Nong Sang</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Recent GRPO-based approaches built on flow matching models have shown remarkable improvements in human preference alignment for text-to-image generation. Nevertheless, they still suffer from the sparse reward problem: the terminal reward of the entire denoising trajectory is applied to all intermediate steps, resulting in a mismatch between the global feedback signals and the exact fine-grained contributions at intermediate denoising steps. To address this issue, we introduce DenseGRPO, a novel framework that aligns human preference with dense rewards, which evaluates the fine-grained contribution of each denoising step. Specifically, our approach includes two key components: (1) we propose to predict the step-wise reward gain as dense reward of each denoising step, which applies a reward model on the intermediate clean images via an ODE-based approach. This manner ensures an alignment between feedback signals and the contributions of individual steps, facilitating effective training; and (2) based on the estimated dense rewards, a mismatch drawback between the uniform exploration setting and the time-varying noise intensity in existing GRPO-based methods is revealed, leading to an inappropriate exploration space. Thus, we propose a reward-aware scheme to calibrate the exploration space by adaptively adjusting a timestep-specific stochasticity injection in the SDE sampler, ensuring a suitable exploration space at all timesteps. Extensive experiments on multiple standard benchmarks demonstrate the effectiveness of the proposed DenseGRPO and highlight the critical role of the valid dense rewards in flow matching model alignment.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.20218</guid>
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<pubDate>Wed, 28 Jan 2026 03:39:05 +0000</pubDate>
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</item>
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<item>
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<title>Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning</title>
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<link>https://arxiv.org/abs/2601.21037</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21037.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chengzu Li, Zanyi Wang, Jiaang Li, Yi Xu, Han Zhou, Huanyu Zhang, Ruichuan An, Dengyang Jiang, Zhaochong An, Ivan Vulić, Serge Belongie, Anna Korhonen</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work, we formulate visual reasoning by means of video generation models, positing that generated frames can act as intermediate reasoning steps between initial states and solutions. We evaluate their capacity in two distinct regimes: Maze Navigation for sequential discrete planning with low visual change and Tangram Puzzle for continuous manipulation with high visual change. Our experiments reveal three critical insights: (1) Robust Zero-Shot Generalization: In both tasks, the model demonstrates strong performance on unseen data distributions without specific finetuning. (2) Visual Context: The model effectively uses visual context as explicit control, such as agent icons and tangram shapes, enabling it to maintain high visual consistency and adapt its planning capability robustly to unseen patterns. (3) Visual Test-Time Scaling: We observe a test-time scaling law in sequential planning; increasing the generated video length (visual inference budget) empowers better zero-shot generalization to spatially and temporally complex paths. These findings suggest that video generation is not merely a media tool, but a scalable, generalizable paradigm for visual reasoning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21037</guid>
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<pubDate>Wed, 28 Jan 2026 20:57:55 +0000</pubDate>
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</item>
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<item>
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<title>Grounding and Enhancing Informativeness and Utility in Dataset Distillation</title>
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<link>https://arxiv.org/abs/2601.21296</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21296.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shaobo Wang, Yantai Yang, Guo Chen, Peiru Li, Kaixin Li, Yufa Zhou, Zhaorun Chen, Linfeng Zhang</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on heuristic approaches to balance efficiency and quality, the fundamental relationship between original and synthetic data remains underexplored. This paper revisits knowledge distillation-based dataset distillation within a solid theoretical framework. We introduce the concepts of Informativeness and Utility, capturing crucial information within a sample and essential samples in the training set, respectively. Building on these principles, we define optimal dataset distillation mathematically. We then present InfoUtil, a framework that balances informativeness and utility in synthesizing the distilled dataset. InfoUtil incorporates two key components: (1) game-theoretic informativeness maximization using Shapley Value attribution to extract key information from samples, and (2) principled utility maximization by selecting globally influential samples based on Gradient Norm. These components ensure that the distilled dataset is both informative and utility-optimized. Experiments demonstrate that our method achieves a 6.1\% performance improvement over the previous state-of-the-art approach on ImageNet-1K dataset using ResNet-18.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21296</guid>
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<pubDate>Thu, 29 Jan 2026 05:49:17 +0000</pubDate>
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</item>
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<item>
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<title>MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning</title>
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<link>https://arxiv.org/abs/2601.21468</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21468.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yaorui Shi, Shugui Liu, Yu Yang, Wenyu Mao, Yuxin Chen, Qi GU, Hui Su, Xunliang Cai, Xiang Wang, An Zhang</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memory systems serialize history as text, where token-level cost is uniform and scales linearly with length, often spending scarce budget on low-value details. To this end, we introduce MemOCR, a multimodal memory agent that improves long-horizon reasoning under tight context budgets by allocating memory space with adaptive information density through visual layout. Concretely, MemOCR maintains a structured rich-text memory (e.g., headings, highlights) and renders it into an image that the agent consults for memory access, visually prioritizing crucial evidence while aggressively compressing auxiliary details. To ensure robustness across varying memory budgets, we train MemOCR with reinforcement learning under budget-aware objectives that expose the agent to diverse compression levels. Across long-context multi-hop and single-hop question-answering benchmarks, MemOCR outperforms strong text-based baselines and achieves more effective context utilization under extreme budgets.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21468</guid>
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<pubDate>Thu, 29 Jan 2026 09:47:17 +0000</pubDate>
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</item>
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<title>ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas</title>
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<link>https://arxiv.org/abs/2601.21558</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21558.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiaoyu Tian, Haotian Wang, Shuaiting Chen, Hao Zhou, Kaichi Yu, Yudian Zhang, Jade Ouyang, Junxi Yin, Jiong Chen, Baoyan Guo, Lei Zhang, Junjie Tao, Yuansheng Song, Ming Cui, Chengwei Liu</p><p><b>Upvotes:</b> 58</p><p><b>Summary:</b> Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing methods still require manual intervention, depend on non-verifiable simulated environments, rely exclusively on either supervised fine-tuning (SFT) or reinforcement learning (RL), and struggle with stable long-horizon, multi-turn learning. To address these challenges, we introduce ASTRA, a fully automated end-to-end framework for training tool-augmented language model agents via scalable data synthesis and verifiable reinforcement learning. ASTRA integrates two complementary components. First, a pipeline that leverages the static topology of tool-call graphs synthesizes diverse, structurally grounded trajectories, instilling broad and transferable tool-use competence. Second, an environment synthesis framework that captures the rich, compositional topology of human semantic reasoning converts decomposed question-answer traces into independent, code-executable, and rule-verifiable environments, enabling deterministic multi-turn RL. Based on this method, we develop a unified training methodology that integrates SFT with online RL using trajectory-level rewards to balance task completion and interaction efficiency. Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance at comparable scales, approaching closed-source systems while preserving core reasoning ability. We release the full pipelines, environments, and trained models at https://github.com/LianjiaTech/astra.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21558</guid>
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<pubDate>Thu, 29 Jan 2026 11:22:23 +0000</pubDate>
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<title>Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities</title>
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<link>https://arxiv.org/abs/2601.21937</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21937.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuangshuang Ying, Zheyu Wang, Yunjian Peng, Jin Chen, Yuhao Wu, Hongbin Lin, Dingyu He, Siyi Liu, Gengchen Yu, YinZhu Piao, Yuchen Wu, Xin Gui, Zhongyuan Peng, Xin Li, Xeron Du, Libo Qin, YiXin Cao, Ge Zhang, Stephen Huang</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Despite strong performance on existing benchmarks, it remains unclear whether large language models can reason over genuinely novel scientific information. Most evaluations score end-to-end RAG pipelines, where reasoning is confounded with retrieval and toolchain choices, and the signal is further contaminated by parametric memorization and open-web volatility. We introduce DeR2, a controlled deep-research sandbox that isolates document-grounded reasoning while preserving core difficulties of deep search: multi-step synthesis, denoising, and evidence-based conclusion making. DeR2 decouples evidence access from reasoning via four regimes--Instruction-only, Concepts (gold concepts without documents), Related-only (only relevant documents), and Full-set (relevant documents plus topically related distractors)--yielding interpretable regime gaps that operationalize retrieval loss vs. reasoning loss and enable fine-grained error attribution. To prevent parametric leakage, we apply a two-phase validation that requires parametric failure without evidence while ensuring oracle-concept solvability. To ensure reproducibility, each instance provides a frozen document library (drawn from 2023-2025 theoretical papers) with expert-annotated concepts and validated rationales. Experiments across a diverse set of state-of-the-art foundation models reveal substantial variation and significant headroom: some models exhibit mode-switch fragility, performing worse with the Full-set than with Instruction-only, while others show structural concept misuse, correctly naming concepts but failing to execute them as procedures.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21937</guid>
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<pubDate>Thu, 29 Jan 2026 16:26:19 +0000</pubDate>
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<title>PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing</title>
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<link>https://arxiv.org/abs/2601.21957</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21957.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Cheng Cui, Ting Sun, Suyin Liang, Tingquan Gao, Zelun Zhang, Jiaxuan Liu, Xueqing Wang, Changda Zhou, Hongen Liu, Manhui Lin, Yue Zhang, Yubo Zhang, Yi Liu, Dianhai Yu, Yanjun Ma</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-photography, and illumination, we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model's capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency. Code: https://github.com/PaddlePaddle/PaddleOCR</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21957</guid>
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<pubDate>Thu, 29 Jan 2026 16:35:04 +0000</pubDate>
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<title>Causal World Modeling for Robot Control</title>
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<link>https://arxiv.org/abs/2601.21998</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.21998.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, Yujun Shen, Yinghao Xu</p><p><b>Upvotes:</b> 28</p><p><b>Summary:</b> This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics. Inspired by this, we introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously. Our model features three carefully crafted designs: (1) a shared latent space, integrating vision and action tokens, driven by a Mixture-of-Transformers (MoT) architecture, (2) a closed-loop rollout mechanism, allowing for ongoing acquisition of environmental feedback with ground-truth observations, (3) an asynchronous inference pipeline, parallelizing action prediction and motor execution to support efficient control. We evaluate our model on both simulation benchmarks and real-world scenarios, where it shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations. The code and model are made publicly available to facilitate the community.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.21998</guid>
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<pubDate>Thu, 29 Jan 2026 17:07:43 +0000</pubDate>
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<title>CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty</title>
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<link>https://arxiv.org/abs/2601.22027</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22027.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Johannes Kirmayr, Lukas Stappen, Elisabeth André</p><p><b>Upvotes:</b> 73</p><p><b>Summary:</b> Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. In domains, such as in-car voice assistants, users often issue incomplete or ambiguous requests, creating intrinsic uncertainty that agents must manage through dialogue, tool use, and policy adherence. We introduce CAR-bench, a benchmark for evaluating consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. The environment features an LLM-simulated user, domain policies, and 58 interconnected tools spanning navigation, productivity, charging, and vehicle control. Beyond standard task completion, CAR-bench introduces Hallucination tasks that test agents' limit-awareness under missing tools or information, and Disambiguation tasks that require resolving uncertainty through clarification or internal information gathering. Baseline results reveal large gaps between occasional and consistent success on all task types. Even frontier reasoning LLMs achieve less than 50% consistent pass rate on Disambiguation tasks due to premature actions, and frequently violate policies or fabricate information to satisfy user requests in Hallucination tasks, underscoring the need for more reliable and self-aware LLM agents in real-world settings.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22027</guid>
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<pubDate>Thu, 29 Jan 2026 17:33:42 +0000</pubDate>
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<title>Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models</title>
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<link>https://arxiv.org/abs/2601.22060</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22060.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wenxuan Huang, Yu Zeng, Qiuchen Wang, Zhen Fang, Shaosheng Cao, Zheng Chu, Qingyu Yin, Shuang Chen, Zhenfei Yin, Lin Chen, Zehui Chen, Yao Hu, Philip Torr, Feng Zhao, Wanli Ouyang</p><p><b>Upvotes:</b> 149</p><p><b>Summary:</b> Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full-level or entity-level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real-world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence from diverse visual and textual sources. Building on this, we propose Vision-DeepResearch, which proposes one new multimodal deep-research paradigm, i.e., performs multi-turn, multi-entity and multi-scale visual and textual search to robustly hit real-world search engines under heavy noise. Our Vision-DeepResearch supports dozens of reasoning steps and hundreds of engine interactions, while internalizing deep-research capabilities into the MLLM via cold-start supervision and RL training, resulting in a strong end-to-end multimodal deep-research MLLM. It substantially outperforming existing multimodal deep-research MLLMs, and workflows built on strong closed-source foundation model such as GPT-5, Gemini-2.5-pro and Claude-4-Sonnet. The code will be released in https://github.com/Osilly/Vision-DeepResearch.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22060</guid>
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<pubDate>Thu, 29 Jan 2026 17:58:40 +0000</pubDate>
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<title>TTCS: Test-Time Curriculum Synthesis for Self-Evolving</title>
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<link>https://arxiv.org/abs/2601.22628</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22628.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chengyi Yang, Zhishang Xiang, Yunbo Tang, Zongpei Teng, Chengsong Huang, Fei Long, Yuhan Liu, Jinsong Su</p><p><b>Upvotes:</b> 32</p><p><b>Summary:</b> Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high-quality pseudo-labels, and the limited size of test sets makes continuous online updates prone to instability. To address these limitations, we propose TTCS, a co-evolving test-time training framework. Specifically, TTCS initializes two policies from the same pretrained model: a question synthesizer and a reasoning solver. These policies evolve through iterative optimization: the synthesizer generates progressively challenging question variants conditioned on the test questions, creating a structured curriculum tailored to the solver's current capability, while the solver updates itself using self-consistency rewards computed from multiple sampled responses on both original test and synthetic questions. Crucially, the solver's feedback guides the synthesizer to generate questions aligned with the model's current capability, and the generated question variants in turn stabilize the solver's test-time training. Experiments show that TTCS consistently strengthens the reasoning ability on challenging mathematical benchmarks and transfers to general-domain tasks across different LLM backbones, highlighting a scalable path towards dynamically constructing test-time curricula for self-evolving. Our code and implementation details are available at https://github.com/XMUDeepLIT/TTCS.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22628</guid>
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<pubDate>Fri, 30 Jan 2026 06:38:02 +0000</pubDate>
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<title>Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling</title>
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<link>https://arxiv.org/abs/2601.22636</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22636.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mingqian Feng, Xiaodong Liu, Weiwei Yang, Chenliang Xu, Christopher White, Jianfeng Gao</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates real-world risk. In practice, attackers can exploit large-scale parallel sampling to repeatedly probe a model until a harmful response is produced. While recent work shows that attack success increases with repeated sampling, principled methods for predicting large-scale adversarial risk remain limited. We propose a scaling-aware Best-of-N estimation of risk, SABER, for modeling jailbreak vulnerability under Best-of-N sampling. We model sample-level success probabilities using a Beta distribution, the conjugate prior of the Bernoulli distribution, and derive an analytic scaling law that enables reliable extrapolation of large-N attack success rates from small-budget measurements. Using only n=100 samples, our anchored estimator predicts ASR@1000 with a mean absolute error of 1.66, compared to 12.04 for the baseline, which is an 86.2% reduction in estimation error. Our results reveal heterogeneous risk scaling profiles and show that models appearing robust under standard evaluation can experience rapid nonlinear risk amplification under parallel adversarial pressure. This work provides a low-cost, scalable methodology for realistic LLM safety assessment. We will release our code and evaluation scripts upon publication to future research.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22636</guid>
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<pubDate>Fri, 30 Jan 2026 06:54:35 +0000</pubDate>
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<title>Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation</title>
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<link>https://arxiv.org/abs/2601.22813</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22813.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Andrei Panferov, Erik Schultheis, Soroush Tabesh, Dan Alistarh</p><p><b>Upvotes:</b> 54</p><p><b>Summary:</b> The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized pre-training of massive models such as LLMs. Yet, existing quantized training methods still sacrifice some of the representation capacity of this format in favor of more accurate unbiased quantized gradient estimation by stochastic rounding (SR), losing noticeable accuracy relative to standard FP16 and FP8 training. In this paper, improve the state of the art for quantized training in NVFP4 via a novel unbiased quantization routine for micro-scaled formats, called MS-EDEN, that has more than 2x lower quantization error than SR. We integrate it into a novel fully-NVFP4 quantization scheme for linear layers, called Quartet II. We show analytically that Quartet II achieves consistently better gradient estimation across all major matrix multiplications, both on the forward and on the backward passes. In addition, our proposal synergizes well with recent training improvements aimed specifically at NVFP4. We further validate Quartet II on end-to-end LLM training with up to 1.9B parameters on 38B tokens. We provide kernels for execution on NVIDIA Blackwell GPUs with up to 4.2x speedup over BF16. Our code is available at https://github.com/IST-DASLab/Quartet-II .</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22813</guid>
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<pubDate>Fri, 30 Jan 2026 10:39:11 +0000</pubDate>
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<title>MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering</title>
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<link>https://arxiv.org/abs/2601.22859</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22859.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chuanzhe Guo, Jingjing Wu, Sijun He, Yang Chen, Zhaoqi Kuang, Shilong Fan, Bingjin Chen, Siqi Bao, Jing Liu, Hua Wu, Qingfu Zhu, Wanxiang Che, Haifeng Wang</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introduce MEnvAgent, a Multi-language framework for automated Environment construction that facilitates scalable generation of verifiable task instances. MEnvAgent employs a multi-agent Planning-Execution-Verification architecture to autonomously resolve construction failures and integrates a novel Environment Reuse Mechanism that reduces computational overhead by incrementally patching historical environments. Evaluations on MEnvBench, a new benchmark comprising 1,000 tasks across 10 languages, demonstrate that MEnvAgent outperforms baselines, improving Fail-to-Pass (F2P) rates by 8.6% while reducing time costs by 43%. Additionally, we demonstrate the utility of MEnvAgent by constructing MEnvData-SWE, the largest open-source polyglot dataset of realistic verifiable Docker environments to date, alongside solution trajectories that enable consistent performance gains on SWE tasks across a wide range of models. Our code, benchmark, and dataset are available at https://github.com/ernie-research/MEnvAgent.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22859</guid>
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<pubDate>Fri, 30 Jan 2026 11:36:10 +0000</pubDate>
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<title>DINO-SAE: DINO Spherical Autoencoder for High-Fidelity Image Reconstruction and Generation</title>
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<link>https://arxiv.org/abs/2601.22904</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22904.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hun Chang, Byunghee Cha, Jong Chul Ye</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generative performance. Unfortunately, existing approaches often suffer from limited reconstruction fidelity due to the loss of high-frequency details. In this work, we present the DINO Spherical Autoencoder (DINO-SAE), a framework that bridges semantic representation and pixel-level reconstruction. Our key insight is that semantic information in contrastive representations is primarily encoded in the direction of feature vectors, while forcing strict magnitude matching can hinder the encoder from preserving fine-grained details. To address this, we introduce Hierarchical Convolutional Patch Embedding module that enhances local structure and texture preservation, and Cosine Similarity Alignment objective that enforces semantic consistency while allowing flexible feature magnitudes for detail retention. Furthermore, leveraging the observation that SSL-based foundation model representations intrinsically lie on a hypersphere, we employ Riemannian Flow Matching to train a Diffusion Transformer (DiT) directly on this spherical latent manifold. Experiments on ImageNet-1K demonstrate that our approach achieves state-of-the-art reconstruction quality, reaching 0.37 rFID and 26.2 dB PSNR, while maintaining strong semantic alignment to the pretrained VFM. Notably, our Riemannian Flow Matching-based DiT exhibits efficient convergence, achieving a gFID of 3.47 at 80 epochs.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22904</guid>
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<pubDate>Fri, 30 Jan 2026 12:25:34 +0000</pubDate>
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<title>Residual Context Diffusion Language Models</title>
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<link>https://arxiv.org/abs/2601.22954</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22954.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuezhou Hu, Harman Singh, Monishwaran Maheswaran, Haocheng Xi, Coleman Hooper, Jintao Zhang, Aditya Tomar, Michael W. Mahoney, Sewon Min, Mehrdad Farajtabar, Kurt Keutzer, Amir Gholami, Chenfeng Xu</p><p><b>Upvotes:</b> 30</p><p><b>Summary:</b> Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art block-wise dLLMs rely on a "remasking" mechanism that decodes only the most confident tokens and discards the rest, effectively wasting computation. We demonstrate that recycling computation from the discarded tokens is beneficial, as these tokens retain contextual information useful for subsequent decoding iterations. In light of this, we propose Residual Context Diffusion (RCD), a module that converts these discarded token representations into contextual residuals and injects them back for the next denoising step. RCD uses a decoupled two-stage training pipeline to bypass the memory bottlenecks associated with backpropagation. We validate our method on both long CoT reasoning (SDAR) and short CoT instruction following (LLaDA) models. We demonstrate that a standard dLLM can be efficiently converted to the RCD paradigm with merely ~1 billion tokens. RCD consistently improves frontier dLLMs by 5-10 points in accuracy with minimal extra computation overhead across a wide range of benchmarks. Notably, on the most challenging AIME tasks, RCD nearly doubles baseline accuracy and attains up to 4-5x fewer denoising steps at equivalent accuracy levels.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22954</guid>
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<pubDate>Fri, 30 Jan 2026 13:16:32 +0000</pubDate>
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<title>Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text</title>
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<link>https://arxiv.org/abs/2601.22975</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.22975.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ximing Lu, David Acuna, Jaehun Jung, Jian Hu, Di Zhang, Shizhe Diao, Yunheng Zou, Shaokun Zhang, Brandon Cui, Mingjie Liu, Hyunwoo Kim, Prithviraj Ammanabrolu, Jan Kautz, Yi Dong, Yejin Choi</p><p><b>Upvotes:</b> 87</p><p><b>Summary:</b> Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet, scaling up RL is bottlenecked by limited existing verifiable data, where improvements increasingly saturate over prolonged training. To overcome this, we propose Golden Goose, a simple trick to synthesize unlimited RLVR tasks from unverifiable internet text by constructing a multiple-choice question-answering version of the fill-in-the-middle task. Given a source text, we prompt an LLM to identify and mask key reasoning steps, then generate a set of diverse, plausible distractors. This enables us to leverage reasoning-rich unverifiable corpora typically excluded from prior RLVR data construction (e.g., science textbooks) to synthesize GooseReason-0.7M, a large-scale RLVR dataset with over 0.7 million tasks spanning mathematics, programming, and general scientific domains. Empirically, GooseReason effectively revives models saturated on existing RLVR data, yielding robust, sustained gains under continuous RL and achieving new state-of-the-art results for 1.5B and 4B-Instruct models across 15 diverse benchmarks. Finally, we deploy Golden Goose in a real-world setting, synthesizing RLVR tasks from raw FineWeb scrapes for the cybersecurity domain, where no prior RLVR data exists. Training Qwen3-4B-Instruct on the resulting data GooseReason-Cyber sets a new state-of-the-art in cybersecurity, surpassing a 7B domain-specialized model with extensive domain-specific pre-training and post-training. This highlights the potential of automatically scaling up RLVR data by exploiting abundant, reasoning-rich, unverifiable internet text.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.22975</guid>
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<pubDate>Fri, 30 Jan 2026 13:39:11 +0000</pubDate>
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<title>THINKSAFE: Self-Generated Safety Alignment for Reasoning Models</title>
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<link>https://arxiv.org/abs/2601.23143</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.23143.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Seanie Lee, Sangwoo Park, Yumin Choi, Gyeongman Kim, Minki Kang, Jihun Yun, Dongmin Park, Jongho Park, Sung Ju Hwang</p><p><b>Upvotes:</b> 38</p><p><b>Summary:</b> Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-thought (CoT) reasoning. However, this over-optimization often prioritizes compliance, making models vulnerable to harmful prompts. To mitigate this safety degradation, recent approaches rely on external teacher distillation, yet this introduces a distributional discrepancy that degrades native reasoning. We propose ThinkSafe, a self-generated alignment framework that restores safety alignment without external teachers. Our key insight is that while compliance suppresses safety mechanisms, models often retain latent knowledge to identify harm. ThinkSafe unlocks this via lightweight refusal steering, guiding the model to generate in-distribution safety reasoning traces. Fine-tuning on these self-generated responses effectively realigns the model while minimizing distribution shift. Experiments on DeepSeek-R1-Distill and Qwen3 show ThinkSafe significantly improves safety while preserving reasoning proficiency. Notably, it achieves superior safety and comparable reasoning to GRPO, with significantly reduced computational cost. Code, models, and datasets are available at https://github.com/seanie12/ThinkSafe.git.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.23143</guid>
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<pubDate>Fri, 30 Jan 2026 16:31:02 +0000</pubDate>
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<title>FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation</title>
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<link>https://arxiv.org/abs/2601.23182</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.23182.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Siyang He, Qiqi Wang, Xiaoran Liu, Hongnan Ma, Yiwei Shi, Yuerong Song, Ying Zhu, Tianyi Liang, Zengfeng Huang, Ziwei He, Xipeng Qiu</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to fully unlock the potential of arbitrary generation. In this work, we delve into the inherent spectral characteristics of dLLMs and present the first frequency-domain analysis showing that low-frequency components in hidden states primarily encode global structural information and long-range dependencies, while high-frequency components are responsible for characterizing local details. Based on this observation, we propose FourierSampler, which leverages a frequency-domain sliding window mechanism to dynamically guide the model to achieve a "structure-to-detail" generation. FourierSampler outperforms other inference enhancement strategies on LLADA and SDAR, achieving relative improvements of 20.4% on LLaDA1.5-8B and 16.0% on LLaDA-8B-Instruct. It notably surpasses similarly sized autoregressive models like Llama3.1-8B-Instruct.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.23182</guid>
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<pubDate>Fri, 30 Jan 2026 17:06:41 +0000</pubDate>
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<title>ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought</title>
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<link>https://arxiv.org/abs/2601.23184</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.23184.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fanmeng Wang, Haotian Liu, Guojiang Zhao, Hongteng Xu, Zhifeng Gao</p><p><b>Upvotes:</b> 34</p><p><b>Summary:</b> While Chain-of-Thought (CoT) significantly enhances the performance of Large Language Models (LLMs), explicit reasoning chains introduce substantial computational redundancy. Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into latent space, but often suffer from severe performance degradation due to the lack of appropriate compression guidance. In this study, we propose Rendered CoT-Guided variational Latent Reasoning (ReGuLaR), a simple yet novel latent learning paradigm resolving this issue. Fundamentally, we formulate latent reasoning within the Variational Auto-Encoding (VAE) framework, sampling the current latent reasoning state from the posterior distribution conditioned on previous ones. Specifically, when learning this variational latent reasoning model, we render explicit reasoning chains as images, from which we extract dense visual-semantic representations to regularize the posterior distribution, thereby achieving efficient compression with minimal information loss. Extensive experiments demonstrate that ReGuLaR significantly outperforms existing latent reasoning methods across both computational efficiency and reasoning effectiveness, and even surpasses CoT through multi-modal reasoning, providing a new and insightful solution to latent reasoning. Code: https://github.com/FanmengWang/ReGuLaR.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.23184</guid>
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<pubDate>Fri, 30 Jan 2026 17:08:06 +0000</pubDate>
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<title>PaperBanana: Automating Academic Illustration for AI Scientists</title>
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<link>https://arxiv.org/abs/2601.23265</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.23265.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon</p><p><b>Upvotes:</b> 149</p><p><b>Summary:</b> Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations. Powered by state-of-the-art VLMs and image generation models, PaperBanana orchestrates specialized agents to retrieve references, plan content and style, render images, and iteratively refine via self-critique. To rigorously evaluate our framework, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications, covering diverse research domains and illustration styles. Comprehensive experiments demonstrate that PaperBanana consistently outperforms leading baselines in faithfulness, conciseness, readability, and aesthetics. We further show that our method effectively extends to the generation of high-quality statistical plots. Collectively, PaperBanana paves the way for the automated generation of publication-ready illustrations.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.23265</guid>
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<pubDate>Fri, 30 Jan 2026 18:33:37 +0000</pubDate>
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<title>Green-VLA: Staged Vision-Language-Action Model for Generalist Robots</title>
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<link>https://arxiv.org/abs/2602.00919</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.00919.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> I. Apanasevich, M. Artemyev, R. Babakyan, P. Fedotova, D. Grankin, E. Kupryashin, A. Misailidi, D. Nerus, A. Nutalapati, G. Sidorov, I. Efremov, M. Gerasyov, D. Pikurov, Y. Senchenko, S. Davidenko, D. Kulikov, M. Sultankin, K. Askarbek, O. Shamanin, D. Statovoy, E. Zalyaev, I. Zorin, A. Letkin, E. Rusakov, A. Silchenko, V. Vorobyov, S. Sobolnikov, A. Postnikov</p><p><b>Upvotes:</b> 258</p><p><b>Summary:</b> We introduce Green-VLA, a staged Vision-Language-Action (VLA) framework for real-world deployment on the Green humanoid robot while maintaining generalization across diverse embodiments. Green-VLA follows a five stage curriculum: (L0) foundational VLMs, (L1) multimodal grounding, (R0) multi-embodiment pretraining, (R1) embodiment-specific adaptation, and (R2) reinforcement-learning (RL) policy alignment. We couple a scalable data-processing pipeline (3,000 hours of demonstrations) with temporal alignment and quality filtering, and use a unified, embodiment-aware action interface enabling a single policy to control humanoids, mobile manipulators, and fixed-base arms. At inference, the VLA controller is enhanced with episode-progress prediction, out-of-distribution detection, and joint-prediction-based guidance to improve safety and precise target selection. Experiments on Simpler BRIDGE WidowX and CALVIN ABC-D, as well as real-robot evaluations, show strong generalization and performance gains from RL alignment in success rate, robustness, and long-horizon efficiency.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.00919</guid>
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<pubDate>Sat, 31 Jan 2026 22:13:23 +0000</pubDate>
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<title>Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2602.01058</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01058.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dylan Zhang, Yufeng Xu, Haojin Wang, Qingzhi Chen, Hao Peng</p><p><b>Upvotes:</b> 39</p><p><b>Summary:</b> Post-training of reasoning LLMs is a holistic process that typically consists of an offline SFT stage followed by an online reinforcement learning (RL) stage. However, SFT is often optimized in isolation to maximize SFT performance alone. We show that, after identical RL training, models initialized from stronger SFT checkpoints can significantly underperform those initialized from weaker ones. We attribute this to a mismatch typical in current SFT-RL pipelines: the distribution that generates the offline SFT data can differ substantially from the policy optimized during online RL, which learns from its own rollouts. We propose PEAR (Policy Evaluation-inspired Algorithm for Offline Learning Loss Re-weighting), an SFT-stage method that corrects this mismatch and better prepares the model for RL. PEAR uses importance sampling to reweight the SFT loss, with three variants operating at the token, block, and sequence levels. It can be used to augment standard SFT objectives and incurs little additional training overhead once probabilities for the offline data are collected. We conduct controlled experiments on verifiable reasoning games and mathematical reasoning tasks on Qwen 2.5 and 3 and DeepSeek-distilled models. PEAR consistently improves post-RL performance over canonical SFT, with pass at 8 gains up to a 14.6 percent on AIME2025. Our results suggest that PEAR is an effective step toward more holistic LLM post-training by designing and evaluating SFT with downstream RL in mind rather than in isolation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01058</guid>
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<pubDate>Sun, 01 Feb 2026 06:53:45 +0000</pubDate>
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<title>Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning</title>
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<link>https://arxiv.org/abs/2602.01335</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01335.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Xu, Yuxin Zhang, Juan Cao, Lin Gao, Chunyu Wang, Oliver Deussen, Tong-Yee Lee, Fan Tang</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric. Despite the remarkable progress of generative AI, existing models remain largely confined to pixel-level instruction alignment and surface-level appearance preservation, failing to capture the underlying abstract logic necessary for genuine metaphorical generation. To bridge this gap, we introduce the task of Visual Metaphor Transfer (VMT), which challenges models to autonomously decouple the "creative essence" from a reference image and re-materialize that abstract logic onto a user-specified target subject. We propose a cognitive-inspired, multi-agent framework that operationalizes Conceptual Blending Theory (CBT) through a novel Schema Grammar ("G"). This structured representation decouples relational invariants from specific visual entities, providing a rigorous foundation for cross-domain logic re-instantiation. Our pipeline executes VMT through a collaborative system of specialized agents: a perception agent that distills the reference into a schema, a transfer agent that maintains generic space invariance to discover apt carriers, a generation agent for high-fidelity synthesis and a hierarchical diagnostic agent that mimics a professional critic, performing closed-loop backtracking to identify and rectify errors across abstract logic, component selection, and prompt encoding. Extensive experiments and human evaluations demonstrate that our method significantly outperforms SOTA baselines in metaphor consistency, analogy appropriateness, and visual creativity, paving the way for automated high-impact creative applications in advertising and media. Source code will be made publicly available.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01335</guid>
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<pubDate>Sun, 01 Feb 2026 17:01:36 +0000</pubDate>
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<title>Rethinking Selective Knowledge Distillation</title>
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<link>https://arxiv.org/abs/2602.01395</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01395.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Almog Tavor, Itay Ebenspanger, Neil Cnaan, Mor Geva</p><p><b>Upvotes:</b> 22</p><p><b>Summary:</b> Growing efforts to improve knowledge distillation (KD) in large language models (LLMs) replace dense teacher supervision with selective distillation, which uses a subset of token positions, vocabulary classes, or training samples for supervision. However, it remains unclear which importance signals, selection policies, and their interplay are most effective. In this work, we revisit where and how to distill in autoregressive LLMs. We disentangle selective KD along the position, class, and sample axes and systematically compare importance signals and selection policies. Then, guided by this analysis, we identify underexplored opportunities and introduce student-entropy-guided position selection (SE-KD). Across a suite of benchmarks, SE-KD often improves accuracy, downstream task adherence, and memory efficiency over dense distillation. Extending this approach across the class and sample axes (SE-KD 3X) yields complementary efficiency gains that make offline teacher caching feasible. In practice, this reduces wall time by 70% and peak memory by 18%, while cutting storage usage by 80% over prior methods without sacrificing performance.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01395</guid>
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<pubDate>Sun, 01 Feb 2026 18:58:27 +0000</pubDate>
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<title>Ebisu: Benchmarking Large Language Models in Japanese Finance</title>
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<link>https://arxiv.org/abs/2602.01479</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01479.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xueqing Peng, Ruoyu Xiang, Fan Zhang, Mingzi Song, Mingyang Jiang, Yan Wang, Lingfei Qian, Taiki Hara, Yuqing Guo, Jimin Huang, Junichi Tsujii, Sophia Ananiadou</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Japanese finance combines agglutinative, head-final linguistic structure, mixed writing systems, and high-context communication norms that rely on indirect expression and implicit commitment, posing a substantial challenge for LLMs. We introduce Ebisu, a benchmark for native Japanese financial language understanding, comprising two linguistically and culturally grounded, expert-annotated tasks: JF-ICR, which evaluates implicit commitment and refusal recognition in investor-facing Q&A, and JF-TE, which assesses hierarchical extraction and ranking of nested financial terminology from professional disclosures. We evaluate a diverse set of open-source and proprietary LLMs spanning general-purpose, Japanese-adapted, and financial models. Results show that even state-of-the-art systems struggle on both tasks. While increased model scale yields limited improvements, language- and domain-specific adaptation does not reliably improve performance, leaving substantial gaps unresolved. Ebisu provides a focused benchmark for advancing linguistically and culturally grounded financial NLP. All datasets and evaluation scripts are publicly released.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01479</guid>
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<pubDate>Sun, 01 Feb 2026 23:02:21 +0000</pubDate>
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<title>Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training</title>
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<link>https://arxiv.org/abs/2602.01511</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01511.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ran Xu, Tianci Liu, Zihan Dong, Tony You, Ilgee Hong, Carl Yang, Linjun Zhang, Tao Zhao, Haoyu Wang</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Standard reward models typically predict scalar scores that fail to capture the multifaceted nature of response quality in non-verifiable domains, such as creative writing or open-ended instruction following. To address this limitation, we propose Rubric-ARM, a framework that jointly optimizes a rubric generator and a judge using reinforcement learning from preference feedback. Unlike existing methods that rely on static rubrics or disjoint training pipelines, our approach treats rubric generation as a latent action learned to maximize judgment accuracy. We introduce an alternating optimization strategy to mitigate the non-stationarity of simultaneous updates, providing theoretical analysis that demonstrates how this schedule reduces gradient variance during training. Extensive experiments show that Rubric-ARM achieves state-of-the-art performance among baselines on multiple benchmarks and significantly improves downstream policy alignment in both offline and online reinforcement learning settings.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01511</guid>
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<pubDate>Mon, 02 Feb 2026 00:50:53 +0000</pubDate>
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<title>Making Avatars Interact: Towards Text-Driven Human-Object Interaction for Controllable Talking Avatars</title>
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<link>https://arxiv.org/abs/2602.01538</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01538.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Youliang Zhang, Zhengguang Zhou, Zhentao Yu, Ziyao Huang, Teng Hu, Sen Liang, Guozhen Zhang, Ziqiao Peng, Shunkai Li, Yi Chen, Zixiang Zhou, Yuan Zhou, Qinglin Lu, Xiu Li</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Generating talking avatars is a fundamental task in video generation. Although existing methods can generate full-body talking avatars with simple human motion, extending this task to grounded human-object interaction (GHOI) remains an open challenge, requiring the avatar to perform text-aligned interactions with surrounding objects. This challenge stems from the need for environmental perception and the control-quality dilemma in GHOI generation. To address this, we propose a novel dual-stream framework, InteractAvatar, which decouples perception and planning from video synthesis for grounded human-object interaction. Leveraging detection to enhance environmental perception, we introduce a Perception and Interaction Module (PIM) to generate text-aligned interaction motions. Additionally, an Audio-Interaction Aware Generation Module (AIM) is proposed to synthesize vivid talking avatars performing object interactions. With a specially designed motion-to-video aligner, PIM and AIM share a similar network structure and enable parallel co-generation of motions and plausible videos, effectively mitigating the control-quality dilemma. Finally, we establish a benchmark, GroundedInter, for evaluating GHOI video generation. Extensive experiments and comparisons demonstrate the effectiveness of our method in generating grounded human-object interactions for talking avatars. Project page: https://interactavatar.github.io</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01538</guid>
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<pubDate>Mon, 02 Feb 2026 02:12:09 +0000</pubDate>
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<title>Toward Cognitive Supersensing in Multimodal Large Language Model</title>
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<link>https://arxiv.org/abs/2602.01541</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01541.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Boyi Li, Yifan Shen, Yuanzhe Liu, Yifan Xu, Jiateng Liu, Xinzhuo Li, Zhengyuan Li, Jingyuan Zhu, Yunhan Zhong, Fangzhou Lan, Jianguo Cao, James M. Rehg, Heng Ji, Ismini Lourentzou, Xu Cao</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> Multimodal Large Language Models (MLLMs) have achieved remarkable success in open-vocabulary perceptual tasks, yet their ability to solve complex cognitive problems remains limited, especially when visual details are abstract and require visual memory. Current approaches primarily scale Chain-of-Thought (CoT) reasoning in the text space, even when language alone is insufficient for clear and structured reasoning, and largely neglect visual reasoning mechanisms analogous to the human visuospatial sketchpad and visual imagery. To mitigate this deficiency, we introduce Cognitive Supersensing, a novel training paradigm that endows MLLMs with human-like visual imagery capabilities by integrating a Latent Visual Imagery Prediction (LVIP) head that jointly learns sequences of visual cognitive latent embeddings and aligns them with the answer, thereby forming vision-based internal reasoning chains. We further introduce a reinforcement learning stage that optimizes text reasoning paths based on this grounded visual latent. To evaluate the cognitive capabilities of MLLMs, we present CogSense-Bench, a comprehensive visual question answering (VQA) benchmark assessing five cognitive dimensions. Extensive experiments demonstrate that MLLMs trained with Cognitive Supersensing significantly outperform state-of-the-art baselines on CogSense-Bench and exhibit superior generalization on out-of-domain mathematics and science VQA benchmarks, suggesting that internal visual imagery is potentially key to bridging the gap between perceptual recognition and cognitive understanding. We will open-source the CogSense-Bench and our model weights.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01541</guid>
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<pubDate>Mon, 02 Feb 2026 02:19:50 +0000</pubDate>
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<title>FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents</title>
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<link>https://arxiv.org/abs/2602.01566</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01566.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chiwei Zhu, Benfeng Xu, Mingxuan Du, Shaohan Wang, Xiaorui Wang, Zhendong Mao, Yongdong Zhang</p><p><b>Upvotes:</b> 44</p><p><b>Summary:</b> Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing, and preventing effective test-time scaling. We introduce FS-Researcher, a file-system-based, dual-agent framework that scales deep research beyond the context window via a persistent workspace. Specifically, a Context Builder agent acts as a librarian which browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length. A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts. In this framework, the file system serves as a durable external memory and a shared coordination medium across agents and sessions, enabling iterative refinement beyond the context window. Experiments on two open-ended benchmarks (DeepResearch Bench and DeepConsult) show that FS-Researcher achieves state-of-the-art report quality across different backbone models. Further analyses demonstrate a positive correlation between final report quality and the computation allocated to the Context Builder, validating effective test-time scaling under the file-system paradigm. The code and data are anonymously open-sourced at https://github.com/Ignoramus0817/FS-Researcher.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01566</guid>
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<pubDate>Mon, 02 Feb 2026 03:00:19 +0000</pubDate>
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<title>Generative Visual Code Mobile World Models</title>
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<link>https://arxiv.org/abs/2602.01576</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01576.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Woosung Koh, Sungjun Han, Segyu Lee, Se-Young Yun, Jamin Shin</p><p><b>Upvotes:</b> 39</p><p><b>Summary:</b> Mobile Graphical User Interface (GUI) World Models (WMs) offer a promising path for improving mobile GUI agent performance at train- and inference-time. However, current approaches face a critical trade-off: text-based WMs sacrifice visual fidelity, while the inability of visual WMs in precise text rendering led to their reliance on slow, complex pipelines dependent on numerous external models. We propose a novel paradigm: visual world modeling via renderable code generation, where a single Vision-Language Model (VLM) predicts the next GUI state as executable web code that renders to pixels, rather than generating pixels directly. This combines the strengths of both approaches: VLMs retain their linguistic priors for precise text rendering while their pre-training on structured web code enables high-fidelity visual generation. We introduce gWorld (8B, 32B), the first open-weight visual mobile GUI WMs built on this paradigm, along with a data generation framework (gWorld) that automatically synthesizes code-based training data. In extensive evaluation across 4 in- and 2 out-of-distribution benchmarks, gWorld sets a new pareto frontier in accuracy versus model size, outperforming 8 frontier open-weight models over 50.25x larger. Further analyses show that (1) scaling training data via gWorld yields meaningful gains, (2) each component of our pipeline improves data quality, and (3) stronger world modeling improves downstream mobile GUI policy performance.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01576</guid>
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<pubDate>Mon, 02 Feb 2026 03:12:16 +0000</pubDate>
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<title>Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles</title>
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<link>https://arxiv.org/abs/2602.01590</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01590.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shaohan Wang, Benfeng Xu, Licheng Zhang, Mingxuan Du, Chiwei Zhu, Xiaorui Wang, Zhendong Mao, Yongdong Zhang</p><p><b>Upvotes:</b> 32</p><p><b>Summary:</b> Deep Research Agents (DRAs) have demonstrated remarkable capabilities in autonomous information retrieval and report generation, showing great potential to assist humans in complex research tasks. Current evaluation frameworks primarily rely on LLM-generated references or LLM-derived evaluation dimensions. While these approaches offer scalability, they often lack the reliability of expert-verified content and struggle to provide objective, fine-grained assessments of critical dimensions. To bridge this gap, we introduce Wiki Live Challenge (WLC), a live benchmark that leverages the newest Wikipedia Good Articles (GAs) as expert-level references. Wikipedia's strict standards for neutrality, comprehensiveness, and verifiability serve as a great challenge for DRAs, with GAs representing the pinnacle of which. We curate a dataset of 100 recent Good Articles and propose Wiki Eval, a comprehensive evaluation framework comprising a fine-grained evaluation method with 39 criteria for writing quality and rigorous metrics for factual verifiability. Extensive experiments on various DRA systems demonstrate a significant gap between current DRAs and human expert-level Wikipedia articles, validating the effectiveness of WLC in advancing agent research. We release our benchmark at https://github.com/WangShao2000/Wiki_Live_Challenge</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01590</guid>
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<pubDate>Mon, 02 Feb 2026 03:30:13 +0000</pubDate>
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<title>PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards</title>
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<link>https://arxiv.org/abs/2602.01624</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01624.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Minh-Quan Le, Gaurav Mittal, Cheng Zhao, David Gu, Dimitris Samaras, Mei Chen</p><p><b>Upvotes:</b> 23</p><p><b>Summary:</b> Text-to-video (T2V) generation aims to synthesize videos with high visual quality and temporal consistency that are semantically aligned with input text. Reward-based post-training has emerged as a promising direction to improve the quality and semantic alignment of generated videos. However, recent methods either rely on large-scale human preference annotations or operate on misaligned embeddings from pre-trained vision-language models, leading to limited scalability or suboptimal supervision. We present PISCES, an annotation-free post-training algorithm that addresses these limitations via a novel Dual Optimal Transport (OT)-aligned Rewards module. To align reward signals with human judgment, PISCES uses OT to bridge text and video embeddings at both distributional and discrete token levels, enabling reward supervision to fulfill two objectives: (i) a Distributional OT-aligned Quality Reward that captures overall visual quality and temporal coherence; and (ii) a Discrete Token-level OT-aligned Semantic Reward that enforces semantic, spatio-temporal correspondence between text and video tokens. To our knowledge, PISCES is the first to improve annotation-free reward supervision in generative post-training through the lens of OT. Experiments on both short- and long-video generation show that PISCES outperforms both annotation-based and annotation-free methods on VBench across Quality and Semantic scores, with human preference studies further validating its effectiveness. We show that the Dual OT-aligned Rewards module is compatible with multiple optimization paradigms, including direct backpropagation and reinforcement learning fine-tuning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01624</guid>
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<pubDate>Mon, 02 Feb 2026 04:37:11 +0000</pubDate>
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<title>Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks</title>
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<link>https://arxiv.org/abs/2602.01630</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01630.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bohan Zeng, Kaixin Zhu, Daili Hua, Bozhou Li, Chengzhuo Tong, Yuran Wang, Xinyi Huang, Yifan Dai, Zixiang Zhang, Yifan Yang, Zhou Liu, Hao Liang, Xiaochen Ma, Ruichuan An, Tianyi Bai, Hongcheng Gao, Junbo Niu, Yang Shi, Xinlong Chen, Yue Ding, Minglei Shi, Kai Zeng, Yiwen Tang, Yuanxing Zhang, Pengfei Wan, Xintao Wang, Wentao Zhang</p><p><b>Upvotes:</b> 47</p><p><b>Summary:</b> World models have emerged as a critical frontier in AI research, aiming to enhance large models by infusing them with physical dynamics and world knowledge. The core objective is to enable agents to understand, predict, and interact with complex environments. However, current research landscape remains fragmented, with approaches predominantly focused on injecting world knowledge into isolated tasks, such as visual prediction, 3D estimation, or symbol grounding, rather than establishing a unified definition or framework. While these task-specific integrations yield performance gains, they often lack the systematic coherence required for holistic world understanding. In this paper, we analyze the limitations of such fragmented approaches and propose a unified design specification for world models. We suggest that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation. This work aims to provide a structured perspective to guide future research toward more general, robust, and principled models of the world.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01630</guid>
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<pubDate>Mon, 02 Feb 2026 04:42:44 +0000</pubDate>
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<title>Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation</title>
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<link>https://arxiv.org/abs/2602.01756</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01756.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jun He, Junyan Ye, Zilong Huang, Dongzhi Jiang, Chenjue Zhang, Leqi Zhu, Renrui Zhang, Xiang Zhang, Weijia Li</p><p><b>Upvotes:</b> 22</p><p><b>Summary:</b> While text-to-image generation has achieved unprecedented fidelity, the vast majority of existing models function fundamentally as static text-to-pixel decoders. Consequently, they often fail to grasp implicit user intentions. Although emerging unified understanding-generation models have improved intent comprehension, they still struggle to accomplish tasks involving complex knowledge reasoning within a single model. Moreover, constrained by static internal priors, these models remain unable to adapt to the evolving dynamics of the real world. To bridge these gaps, we introduce Mind-Brush, a unified agentic framework that transforms generation into a dynamic, knowledge-driven workflow. Simulating a human-like 'think-research-create' paradigm, Mind-Brush actively retrieves multimodal evidence to ground out-of-distribution concepts and employs reasoning tools to resolve implicit visual constraints. To rigorously evaluate these capabilities, we propose Mind-Bench, a comprehensive benchmark comprising 500 distinct samples spanning real-time news, emerging concepts, and domains such as mathematical and Geo-Reasoning. Extensive experiments demonstrate that Mind-Brush significantly enhances the capabilities of unified models, realizing a zero-to-one capability leap for the Qwen-Image baseline on Mind-Bench, while achieving superior results on established benchmarks like WISE and RISE.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01756</guid>
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<pubDate>Mon, 02 Feb 2026 07:42:13 +0000</pubDate>
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<title>CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding</title>
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<link>https://arxiv.org/abs/2602.01785</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01785.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuling Shi, Chaoxiang Xie, Zhensu Sun, Yeheng Chen, Chenxu Zhang, Longfei Yun, Chengcheng Wan, Hongyu Zhang, David Lo, Xiaodong Gu</p><p><b>Upvotes:</b> 90</p><p><b>Summary:</b> Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a linear increase in context length and associated computational costs. The rapid advancement of Multimodal LLMs (MLLMs) introduces an opportunity to optimize efficiency by representing source code as rendered images. Unlike text, which is difficult to compress without losing semantic meaning, the image modality is inherently suitable for compression. By adjusting resolution, images can be scaled to a fraction of their original token cost while remaining recognizable to vision-capable models. To explore the feasibility of this approach, we conduct the first systematic study on the effectiveness of MLLMs for code understanding. Our experiments reveal that: (1) MLLMs can effectively understand code with substantial token reduction, achieving up to 8x compression; (2) MLLMs can effectively leverage visual cues such as syntax highlighting, improving code completion performance under 4x compression; and (3) Code-understanding tasks like clone detection exhibit exceptional resilience to visual compression, with some compression ratios even slightly outperforming raw text inputs. Our findings highlight both the potential and current limitations of MLLMs in code understanding, which points out a shift toward image-modality code representation as a pathway to more efficient inference.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01785</guid>
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<pubDate>Mon, 02 Feb 2026 08:10:21 +0000</pubDate>
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<title>Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention</title>
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<link>https://arxiv.org/abs/2602.01801</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01801.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dvir Samuel, Issar Tzachor, Matan Levy, Micahel Green, Gal Chechik, Rami Ben-Ari</p><p><b>Upvotes:</b> 27</p><p><b>Summary:</b> Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines. However, their core attention layers become a major bottleneck at inference time: as generation progresses, the KV cache grows, causing both increasing latency and escalating GPU memory, which in turn restricts usable temporal context and harms long-range consistency. In this work, we study redundancy in autoregressive video diffusion and identify three persistent sources: near-duplicate cached keys across frames, slowly evolving (largely semantic) queries/keys that make many attention computations redundant, and cross-attention over long prompts where only a small subset of tokens matters per frame. Building on these observations, we propose a unified, training-free attention framework for autoregressive diffusion: TempCache compresses the KV cache via temporal correspondence to bound cache growth; AnnCA accelerates cross-attention by selecting frame-relevant prompt tokens using fast approximate nearest neighbor (ANN) matching; and AnnSA sparsifies self-attention by restricting each query to semantically matched keys, also using a lightweight ANN. Together, these modules reduce attention, compute, and memory and are compatible with existing autoregressive diffusion backbones and world models. Experiments demonstrate up to x5--x10 end-to-end speedups while preserving near-identical visual quality and, crucially, maintaining stable throughput and nearly constant peak GPU memory usage over long rollouts, where prior methods progressively slow down and suffer from increasing memory usage.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01801</guid>
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<pubDate>Mon, 02 Feb 2026 08:31:21 +0000</pubDate>
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<title>How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing</title>
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<link>https://arxiv.org/abs/2602.01851</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.01851.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Huanyu Zhang, Xuehai Bai, Chengzu Li, Chen Liang, Haochen Tian, Haodong Li, Ruichuan An, Yifan Zhang, Anna Korhonen, Zhang Zhang, Liang Wang, Tieniu Tan</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> Recent generative models have achieved remarkable progress in image editing. However, existing systems and benchmarks remain largely text-guided. In contrast, human communication is inherently multimodal, where visual instructions such as sketches efficiently convey spatial and structural intent. To address this gap, we introduce VIBE, the Visual Instruction Benchmark for Image Editing with a three-level interaction hierarchy that captures deictic grounding, morphological manipulation, and causal reasoning. Across these levels, we curate high-quality and diverse test cases that reflect progressively increasing complexity in visual instruction following. We further propose a robust LMM-as-a-judge evaluation framework with task-specific metrics to enable scalable and fine-grained assessment. Through a comprehensive evaluation of 17 representative open-source and proprietary image editing models, we find that proprietary models exhibit early-stage visual instruction-following capabilities and consistently outperform open-source models. However, performance degrades markedly with increasing task difficulty even for the strongest systems, highlighting promising directions for future research.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.01851</guid>
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<pubDate>Mon, 02 Feb 2026 09:24:45 +0000</pubDate>
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<title>WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora</title>
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<link>https://arxiv.org/abs/2602.02053</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02053.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Pengyu Wang, Benfeng Xu, Licheng Zhang, Shaohan Wang, Mingxuan Du, Chiwei Zhu, Zhendong Mao</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence across multiple documents. However, many existing benchmarks for GraphRAG rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents. To bridge this gap, we introduce WildGraphBench, a benchmark designed to assess GraphRAG performance in the wild. We leverage Wikipedia's unique structure, where cohesive narratives are grounded in long and heterogeneous external reference documents, to construct a benchmark reflecting real-word scenarios. Specifically, we sample articles across 12 top-level topics, using their external references as the retrieval corpus and citation-linked statements as ground truth, resulting in 1,100 questions spanning three levels of complexity: single-fact QA, multi-fact QA, and section-level summarization. Experiments across multiple baselines reveal that current GraphRAG pipelines help on multi-fact aggregation when evidence comes from a moderate number of sources, but this aggregation paradigm may overemphasize high-level statements at the expense of fine-grained details, leading to weaker performance on summarization tasks. Project page:https://github.com/BstWPY/WildGraphBench.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02053</guid>
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<pubDate>Mon, 02 Feb 2026 12:55:29 +0000</pubDate>
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<title>daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently</title>
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<link>https://arxiv.org/abs/2602.02619</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02619.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mohan Jiang, Dayuan Fu, Junhao Shi, Ji Zeng, Weiye Si, Keyu Li, Xuefeng Li, Yang Xiao, Wenjie Li, Dequan Wang, Pengfei Liu</p><p><b>Upvotes:</b> 48</p><p><b>Summary:</b> While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of training data that captures authentic long-dependency structures and cross-stage evolutionary dynamics--existing synthesis methods either confine to single-feature scenarios constrained by model distribution, or incur prohibitive human annotation costs, failing to provide scalable, high-quality supervision. We address this by reconceptualizing data synthesis through the lens of real-world software evolution. Our key insight: Pull Request (PR) sequences naturally embody the supervision signals for long-horizon learning. They decompose complex objectives into verifiable submission units, maintain functional coherence across iterations, and encode authentic refinement patterns through bug-fix histories. Building on this, we propose daVinci-Agency, which systematically mines structured supervision from chain-of-PRs through three interlocking mechanisms: (1) progressive task decomposition via continuous commits, (2) long-term consistency enforcement through unified functional objectives, and (3) verifiable refinement from authentic bug-fix trajectories. Unlike synthetic trajectories that treat each step independently, daVinci-Agency's PR-grounded structure inherently preserves the causal dependencies and iterative refinements essential for teaching persistent goal-directed behavior and enables natural alignment with project-level, full-cycle task modeling. The resulting trajectories are substantial--averaging 85k tokens and 116 tool calls--yet remarkably data-efficient: fine-tuning GLM-4.6 on 239 daVinci-Agency samples yields broad improvements across benchmarks, notably achieving a 47% relative gain on Toolathlon. Beyond benchmark performance, our analysis confirms...</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02619</guid>
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<pubDate>Mon, 02 Feb 2026 13:23:39 +0000</pubDate>
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<title>Closing the Loop: Universal Repository Representation with RPG-Encoder</title>
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<link>https://arxiv.org/abs/2602.02084</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02084.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jane Luo, Chengyu Yin, Xin Zhang, Qingtao Li, Steven Liu, Yiming Huang, Jie Wu, Hao Liu, Yangyu Huang, Yu Kang, Fangkai Yang, Ying Xin, Scarlett Li</p><p><b>Upvotes:</b> 82</p><p><b>Summary:</b> Current repository agents encounter a reasoning disconnect due to fragmented representations, as existing methods rely on isolated API documentation or dependency graphs that lack semantic depth. We consider repository comprehension and generation to be inverse processes within a unified cycle: generation expands intent into implementation, while comprehension compresses implementation back into intent. To address this, we propose RPG-Encoder, a framework that generalizes the Repository Planning Graph (RPG) from a static generative blueprint into a unified, high-fidelity representation. RPG-Encoder closes the reasoning loop through three mechanisms: (1) Encoding raw code into the RPG that combines lifted semantic features with code dependencies; (2) Evolving the topology incrementally to decouple maintenance costs from repository scale, reducing overhead by 95.7%; and (3) Operating as a unified interface for structure-aware navigation. In evaluations, RPG-Encoder establishes state-of-the-art repository understanding on SWE-bench Verified with 93.7% Acc@5 and exceeds the best baseline by over 10% on SWE-bench Live Lite. These results highlight our superior fine-grained localization accuracy in complex codebases. Furthermore, it achieves 98.5% reconstruction coverage on RepoCraft, confirming RPG's high-fidelity capacity to mirror the original codebase and closing the loop between intent and implementation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02084</guid>
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<pubDate>Mon, 02 Feb 2026 13:30:00 +0000</pubDate>
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<title>FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space</title>
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<link>https://arxiv.org/abs/2602.02092</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02092.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> FSVideo Team, Qingyu Chen, Zhiyuan Fang, Haibin Huang, Xinwei Huang, Tong Jin, Minxuan Lin, Bo Liu, Celong Liu, Chongyang Ma, Xing Mei, Xiaohui Shen, Yaojie Shen, Fuwen Tan, Angtian Wang, Xiao Yang, Yiding Yang, Jiamin Yuan, Lingxi Zhang, Yuxin Zhang</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> We introduce FSVideo, a fast speed transformer-based image-to-video (I2V) diffusion framework. We build our framework on the following key components: 1.) a new video autoencoder with highly-compressed latent space (64times64times4 spatial-temporal downsampling ratio), achieving competitive reconstruction quality; 2.) a diffusion transformer (DIT) architecture with a new layer memory design to enhance inter-layer information flow and context reuse within DIT, and 3.) a multi-resolution generation strategy via a few-step DIT upsampler to increase video fidelity. Our final model, which contains a 14B DIT base model and a 14B DIT upsampler, achieves competitive performance against other popular open-source models, while being an order of magnitude faster. We discuss our model design as well as training strategies in this report.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02092</guid>
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<pubDate>Mon, 02 Feb 2026 13:37:38 +0000</pubDate>
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<title>No Global Plan in Chain-of-Thought: Uncover the Latent Planning Horizon of LLMs</title>
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<link>https://arxiv.org/abs/2602.02103</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02103.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Liyan Xu, Mo Yu, Fandong Meng, Jie Zhou</p><p><b>Upvotes:</b> 66</p><p><b>Summary:</b> This work stems from prior complementary observations on the dynamics of Chain-of-Thought (CoT): Large Language Models (LLMs) is shown latent planning of subsequent reasoning prior to CoT emergence, thereby diminishing the significance of explicit CoT; whereas CoT remains critical for tasks requiring multi-step reasoning. To deepen the understanding between LLM's internal states and its verbalized reasoning trajectories, we investigate the latent planning strength of LLMs, through our probing method, Tele-Lens, applying to hidden states across diverse task domains. Our empirical results indicate that LLMs exhibit a myopic horizon, primarily conducting incremental transitions without precise global planning. Leveraging this characteristic, we propose a hypothesis on enhancing uncertainty estimation of CoT, which we validate that a small subset of CoT positions can effectively represent the uncertainty of the entire path. We further underscore the significance of exploiting CoT dynamics, and demonstrate that automatic recognition of CoT bypass can be achieved without performance degradation. Our code, data and models are released at https://github.com/lxucs/tele-lens.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02103</guid>
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<pubDate>Mon, 02 Feb 2026 13:46:56 +0000</pubDate>
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</item>
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<item>
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<title>Quantifying the Gap between Understanding and Generation within Unified Multimodal Models</title>
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<link>https://arxiv.org/abs/2602.02140</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02140.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chenlong Wang, Yuhang Chen, Zhihan Hu, Dongping Chen, Wenhu Chen, Sarah Wiegreffe, Tianyi Zhou</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Recent advances in unified multimodal models (UMM) have demonstrated remarkable progress in both understanding and generation tasks. However, whether these two capabilities are genuinely aligned and integrated within a single model remains unclear. To investigate this question, we introduce GapEval, a bidirectional benchmark designed to quantify the gap between understanding and generation capabilities, and quantitatively measure the cognitive coherence of the two "unified" directions. Each question can be answered in both modalities (image and text), enabling a symmetric evaluation of a model's bidirectional inference capability and cross-modal consistency. Experiments reveal a persistent gap between the two directions across a wide range of UMMs with different architectures, suggesting that current models achieve only surface-level unification rather than deep cognitive convergence of the two. To further explore the underlying mechanism, we conduct an empirical study from the perspective of knowledge manipulation to illustrate the underlying limitations. Our findings indicate that knowledge within UMMs often remains disjoint. The capability emergence and knowledge across modalities are unsynchronized, paving the way for further exploration.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02140</guid>
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<pubDate>Mon, 02 Feb 2026 14:19:37 +0000</pubDate>
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<title>Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models</title>
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<link>https://arxiv.org/abs/2602.02185</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02185.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, Yufan Shen, Yishuo Cai, Xiaoman Wang, Zhenfei Yin, Lin Chen, Zehui Chen, Shiting Huang, Yiming Zhao, Yao Hu, Philip Torr, Wanli Ouyang, Shaosheng Cao</p><p><b>Upvotes:</b> 124</p><p><b>Summary:</b> Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision-DeepResearch systems that use search engines for complex visual-textual fact-finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, existing benchmarks are not visual search-centric: answers that should require visual search are often leaked through cross-textual cues in the text questions or can be inferred from the prior world knowledge in current MLLMs. Second, overly idealized evaluation scenario: On the image-search side, the required information can often be obtained via near-exact matching against the full image, while the text-search side is overly direct and insufficiently challenging. To address these issues, we construct the Vision-DeepResearch benchmark (VDR-Bench) comprising 2,000 VQA instances. All questions are created via a careful, multi-stage curation pipeline and rigorous expert review, designed to assess the behavior of Vision-DeepResearch systems under realistic real-world conditions. Moreover, to address the insufficient visual retrieval capabilities of current MLLMs, we propose a simple multi-round cropped-search workflow. This strategy is shown to effectively improve model performance in realistic visual retrieval scenarios. Overall, our results provide practical guidance for the design of future multimodal deep-research systems. The code will be released in https://github.com/Osilly/Vision-DeepResearch.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02185</guid>
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<pubDate>Mon, 02 Feb 2026 14:53:11 +0000</pubDate>
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</item>
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<item>
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<title>TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents</title>
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<link>https://arxiv.org/abs/2602.02196</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02196.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hang Yan, Xinyu Che, Fangzhi Xu, Qiushi Sun, Zichen Ding, Kanzhi Cheng, Jian Zhang, Tao Qin, Jun Liu, Qika Lin</p><p><b>Upvotes:</b> 31</p><p><b>Summary:</b> Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time Improvement (TTI). However, the mechanisms under how and why TTI succeed or fail remain poorly understood, and existing evaluation metrics fail to capture their task optimization efficiency, behavior adaptation after erroneous actions, and the specific utility of working memory for task completion. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), an agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions. The framework measures (1) the overall temporal dynamics of task completion and (2) identifies whether performance is primarily constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments across diverse agents and environments, TIDE highlights that improving agent performance requires more than scaling internal reasoning, calling for explicitly optimizing the interaction dynamics between the agent and the environment.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02196</guid>
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<pubDate>Mon, 02 Feb 2026 15:00:47 +0000</pubDate>
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</item>
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<item>
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<title>Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation</title>
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<link>https://arxiv.org/abs/2602.02214</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02214.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hongzhou Zhu, Min Zhao, Guande He, Hang Su, Chongxuan Li, Jun Zhu</p><p><b>Upvotes:</b> 23</p><p><b>Summary:</b> To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an architectural gap when full attention is replaced by causal attention. However, existing approaches do not bridge this gap theoretically. They initialize the AR student via ODE distillation, which requires frame-level injectivity, where each noisy frame must map to a unique clean frame under the PF-ODE of an AR teacher. Distilling an AR student from a bidirectional teacher violates this condition, preventing recovery of the teacher's flow map and instead inducing a conditional-expectation solution, which degrades performance. To address this issue, we propose Causal Forcing that uses an AR teacher for ODE initialization, thereby bridging the architectural gap. Empirical results show that our method outperforms all baselines across all metrics, surpassing the SOTA Self Forcing by 19.3\% in Dynamic Degree, 8.7\% in VisionReward, and 16.7\% in Instruction Following. Project page and the code: https://thu-ml.github.io/CausalForcing.github.io/{https://thu-ml.github.io/CausalForcing.github.io/}</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02214</guid>
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<pubDate>Mon, 02 Feb 2026 15:19:22 +0000</pubDate>
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</item>
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<item>
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<title>Kimi K2.5: Visual Agentic Intelligence</title>
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<link>https://arxiv.org/abs/2602.02276</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02276.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, S. H. Cai, Yuan Cao, Y. Charles, H. S. Che, Cheng Chen, Guanduo Chen, Huarong Chen, Jia Chen, Jiahao Chen, Jianlong Chen, Jun Chen, Kefan Chen, Liang Chen, Ruijue Chen, Xinhao Chen, Yanru Chen, Yanxu Chen, Yicun Chen, Yimin Chen, Yingjiang Chen, Yuankun Chen, Yujie Chen, Yutian Chen, Zhirong Chen, Ziwei Chen, Dazhi Cheng, Minghan Chu, Jialei Cui, Jiaqi Deng, Muxi Diao, Hao Ding, Mengfan Dong, Mengnan Dong, Yuxin Dong, Yuhao Dong, Angang Du, Chenzhuang Du, Dikang Du, Lingxiao Du, Yulun Du, Yu Fan, Shengjun Fang, Qiulin Feng, Yichen Feng, Garimugai Fu, Kelin Fu, Hongcheng Gao, Tong Gao, Yuyao Ge, Shangyi Geng, Chengyang Gong, Xiaochen Gong, Zhuoma Gongque, Qizheng Gu, Xinran Gu, Yicheng Gu, Longyu Guan, Yuanying Guo, Xiaoru Hao, Weiran He, Wenyang He, Yunjia He, Chao Hong, Hao Hu, Jiaxi Hu, Yangyang Hu, Zhenxing Hu, Ke Huang, Ruiyuan Huang, Weixiao Huang, Zhiqi Huang, Tao Jiang, Zhejun Jiang, Xinyi Jin, Yu Jing, Guokun Lai, Aidi Li, C. Li, Cheng Li, Fang Li, Guanghe Li, Guanyu Li, Haitao Li, Haoyang Li, Jia Li, Jingwei Li, Junxiong Li, Lincan Li, Mo Li, Weihong Li, Wentao Li, Xinhang Li, Xinhao Li, Yang Li, Yanhao Li, Yiwei Li, Yuxiao Li, Zhaowei Li, Zheming Li, Weilong Liao, Jiawei Lin, Xiaohan Lin, Zhishan Lin, Zichao Lin, Cheng Liu, Chenyu Liu, Hongzhang Liu, Liang Liu, Shaowei Liu, Shudong Liu, Shuran Liu, Tianwei Liu, Tianyu Liu, Weizhou Liu, Xiangyan Liu, Yangyang Liu, Yanming Liu, Yibo Liu, Yuanxin Liu, Yue Liu, Zhengying Liu, Zhongnuo Liu, Enzhe Lu, Haoyu Lu, Zhiyuan Lu, Junyu Luo, Tongxu Luo, Yashuo Luo, Long Ma, Yingwei Ma, Shaoguang Mao, Yuan Mei, Xin Men, Fanqing Meng, Zhiyong Meng, Yibo Miao, Minqing Ni, Kun Ouyang, Siyuan Pan, Bo Pang, Yuchao Qian, Ruoyu Qin, Zeyu Qin, Jiezhong Qiu, Bowen Qu, Zeyu Shang, Youbo Shao, Tianxiao Shen, Zhennan Shen, Juanfeng Shi, Lidong Shi, Shengyuan Shi, Feifan Song, Pengwei Song, Tianhui Song, Xiaoxi Song, Hongjin Su, Jianlin Su, Zhaochen Su, Lin Sui, Jinsong Sun, Junyao Sun, Tongyu Sun, Flood Sung, Yunpeng Tai, Chuning Tang, Heyi Tang, Xiaojuan Tang, Zhengyang Tang, Jiawen Tao, Shiyuan Teng, Chaoran Tian, Pengfei Tian, Ao Wang, Bowen Wang, Chensi Wang, Chuang Wang, Congcong Wang, Dingkun Wang, Dinglu Wang, Dongliang Wang, Feng Wang, Hailong Wang, Haiming Wang, Hengzhi Wang, Huaqing Wang, Hui Wang, Jiahao Wang, Jinhong Wang, Jiuzheng Wang, Kaixin Wang, Linian Wang, Qibin Wang, Shengjie Wang, Shuyi Wang, Si Wang, Wei Wang, Xiaochen Wang, Xinyuan Wang, Yao Wang, Yejie Wang, Yipu Wang, Yiqin Wang, Yucheng Wang, Yuzhi Wang, Zhaoji Wang, Zhaowei Wang, Zhengtao Wang, Zhexu Wang, Zihan Wang, Zizhe Wang, Chu Wei, Ming Wei, Chuan Wen, Zichen Wen, Chengjie Wu, Haoning Wu, Junyan Wu, Rucong Wu, Wenhao Wu, Yuefeng Wu, Yuhao Wu, Yuxin Wu, Zijian Wu, Chenjun Xiao, Jin Xie, Xiaotong Xie, Yuchong Xie, Yifei Xin, Bowei Xing, Boyu Xu, Jianfan Xu, Jing Xu, Jinjing Xu, L. H. Xu, Lin Xu, Suting Xu, Weixin Xu, Xinbo Xu, Xinran Xu, Yangchuan Xu, Yichang Xu, Yuemeng Xu, Zelai Xu, Ziyao Xu, Junjie Yan, Yuzi Yan, Guangyao Yang, Hao Yang, Junwei Yang, Kai Yang, Ningyuan Yang, Ruihan Yang, Xiaofei Yang, Xinlong Yang, Ying Yang, Yi Yang, Yi Yang, Zhen Yang, Zhilin Yang, Zonghan Yang, Haotian Yao, Dan Ye, Wenjie Ye, Zhuorui Ye, Bohong Yin, Chengzhen Yu, Longhui Yu, Tao Yu, Tianxiang Yu, Enming Yuan, Mengjie Yuan, Xiaokun Yuan, Yang Yue, Weihao Zeng, Dunyuan Zha, Haobing Zhan, Dehao Zhang, Hao Zhang, Jin Zhang, Puqi Zhang, Qiao Zhang, Rui Zhang, Xiaobin Zhang, Y. Zhang, Yadong Zhang, Yangkun Zhang, Yichi Zhang, Yizhi Zhang, Yongting Zhang, Yu Zhang, Yushun Zhang, Yutao Zhang, Yutong Zhang, Zheng Zhang, Chenguang Zhao, Feifan Zhao, Jinxiang Zhao, Shuai Zhao, Xiangyu Zhao, Yikai Zhao, Zijia Zhao, Huabin Zheng, Ruihan Zheng, Shaojie Zheng, Tengyang Zheng, Junfeng Zhong, Longguang Zhong, Weiming Zhong, M. Zhou, Runjie Zhou, Xinyu Zhou, Zaida Zhou, Jinguo Zhu, Liya Zhu, Xinhao Zhu, Yuxuan Zhu, Zhen Zhu, Jingze Zhuang, Weiyu Zhuang, Ying Zou, Xinxing Zu</p><p><b>Upvotes:</b> 208</p><p><b>Summary:</b> We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that two modalities enhance each other. This includes a series of techniques such as joint text-vision pre-training, zero-vision SFT, and joint text-vision reinforcement learning. Building on this multimodal foundation, K2.5 introduces Agent Swarm, a self-directed parallel agent orchestration framework that dynamically decomposes complex tasks into heterogeneous sub-problems and executes them concurrently. Extensive evaluations show that Kimi K2.5 achieves state-of-the-art results across various domains including coding, vision, reasoning, and agentic tasks. Agent Swarm also reduces latency by up to 4.5times over single-agent baselines. We release the post-trained Kimi K2.5 model checkpoint to facilitate future research and real-world applications of agentic intelligence.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02276</guid>
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<pubDate>Mon, 02 Feb 2026 16:17:38 +0000</pubDate>
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</item>
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<item>
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<title>Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs</title>
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<link>https://arxiv.org/abs/2602.02338</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02338.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Liang, Zhongjin Zhang, Yuxuan Zhu, Kerui Zhang, Zhiluohan Guo, Wenhang Zhou, Zonqi Yang, Kangle Wu, Yabo Ni, Anxiang Zeng, Cong Fu, Jianxin Wang, Jiazhi Xia</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item embeddings are learned from foundation models and discretized using generic quantization schemes. This design is misaligned with generative recommendation objectives: semantic embeddings are weakly coupled with collaborative prediction, and generic quantization is inefficient at reducing sequential uncertainty for autoregressive modeling. To address these, we propose ReSID, a recommendation-native, principled SID framework that rethinks representation learning and quantization from the perspective of information preservation and sequential predictability, without relying on LLMs. ReSID consists of two components: (i) Field-Aware Masked Auto-Encoding (FAMAE), which learns predictive-sufficient item representations from structured features, and (ii) Globally Aligned Orthogonal Quantization (GAOQ), which produces compact and predictable SID sequences by jointly reducing semantic ambiguity and prefix-conditional uncertainty. Theoretical analysis and extensive experiments across ten datasets show the effectiveness of ReSID. ReSID consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x. Code is available at https://github.com/FuCongResearchSquad/ReSID.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02338</guid>
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<pubDate>Mon, 02 Feb 2026 17:00:04 +0000</pubDate>
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<item>
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<title>Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics</title>
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<link>https://arxiv.org/abs/2602.02343</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02343.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ziwen Xu, Chenyan Wu, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model's valid-generation manifold. Finally, we introduce a new steering approach SPLIT guided by this analysis that improves preference while better preserving utility. Code is available at https://github.com/zjunlp/EasyEdit/blob/main/examples/SPLIT.md.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02343</guid>
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<pubDate>Mon, 02 Feb 2026 17:04:36 +0000</pubDate>
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<title>SWE-Universe: Scale Real-World Verifiable Environments to Millions</title>
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<link>https://arxiv.org/abs/2602.02361</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02361.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mouxiang Chen, Lei Zhang, Yunlong Feng, Xuwu Wang, Wenting Zhao, Ruisheng Cao, Jiaxi Yang, Jiawei Chen, Mingze Li, Zeyao Ma, Hao Ge, Zongmeng Zhang, Zeyu Cui, Dayiheng Liu, Jingren Zhou, Jianling Sun, Junyang Lin, Binyuan Hui</p><p><b>Upvotes:</b> 59</p><p><b>Summary:</b> We propose SWE-Universe, a scalable and efficient framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs). To overcome the prevalent challenges of automatic building, such as low production yield, weak verifiers, and prohibitive cost, our framework utilizes a building agent powered by an efficient custom-trained model. This agent employs iterative self-verification and in-loop hacking detection to ensure the reliable generation of high-fidelity, verifiable tasks. Using this method, we scale the number of real-world multilingual SWE environments to a million scale (807,693). We demonstrate the profound value of our environments through large-scale agentic mid-training and reinforcement learning. Finally, we applied this technique to Qwen3-Max-Thinking and achieved a score of 75.3% on SWE-Bench Verified. Our work provides both a critical resource and a robust methodology to advance the next generation of coding agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02361</guid>
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<pubDate>Mon, 02 Feb 2026 17:20:30 +0000</pubDate>
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</item>
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<item>
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<title>Unified Personalized Reward Model for Vision Generation</title>
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<link>https://arxiv.org/abs/2602.02380</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02380.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yibin Wang, Yuhang Zang, Feng Han, Jiazi Bu, Yujie Zhou, Cheng Jin, Jiaqi Wang</p><p><b>Upvotes:</b> 19</p><p><b>Summary:</b> Recent advancements in multimodal reward models (RMs) have significantly propelled the development of visual generation. Existing frameworks typically adopt Bradley-Terry-style preference modeling or leverage generative VLMs as judges, and subsequently optimize visual generation models via reinforcement learning. However, current RMs suffer from inherent limitations: they often follow a one-size-fits-all paradigm that assumes a monolithic preference distribution or relies on fixed evaluation rubrics. As a result, they are insensitive to content-specific visual cues, leading to systematic misalignment with subjective and context-dependent human preferences. To this end, inspired by human assessment, we propose UnifiedReward-Flex, a unified personalized reward model for vision generation that couples reward modeling with flexible and context-adaptive reasoning. Specifically, given a prompt and the generated visual content, it first interprets the semantic intent and grounds on visual evidence, then dynamically constructs a hierarchical assessment by instantiating fine-grained criteria under both predefined and self-generated high-level dimensions. Our training pipeline follows a two-stage process: (1) we first distill structured, high-quality reasoning traces from advanced closed-source VLMs to bootstrap SFT, equipping the model with flexible and context-adaptive reasoning behaviors; (2) we then perform direct preference optimization (DPO) on carefully curated preference pairs to further strengthen reasoning fidelity and discriminative alignment. To validate the effectiveness, we integrate UnifiedReward-Flex into the GRPO framework for image and video synthesis, and extensive results demonstrate its superiority.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02380</guid>
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<pubDate>Mon, 02 Feb 2026 17:44:21 +0000</pubDate>
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</item>
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<item>
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<title>SLIME: Stabilized Likelihood Implicit Margin Enforcement for Preference Optimization</title>
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<link>https://arxiv.org/abs/2602.02383</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02383.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Maksim Afanasyev, Illarion Iov</p><p><b>Upvotes:</b> 28</p><p><b>Summary:</b> Direct preference optimization methods have emerged as a computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) for aligning Large Language Models (LLMs). Latest approaches have streamlined the alignment process by deriving implicit reward functions, yet they often suffer from a critical objective mismatch: optimizing the relative margin between chosen and rejected responses does not guarantee the preservation of the chosen response's absolute likelihood. This can lead to ``unlearning'', where the model degrades the probability of high-quality outputs to satisfy margin constraints, and ``formatting collapse'' caused by the over-penalization of rejected sequences. In this work, we introduce SLIME (Stabilized Likelihood Implicit Margin Enforcement), a reference-free alignment objective designed to decouple preference learning from generation quality. SLIME incorporates a three-pronged objective: (1) an anchoring term to maximize the likelihood of preferred responses; (2) a stabilizing penalty that prevents the probabilities of rejected tokens from collapsing to zero; and (3) a dual-margin mechanism that combines hard and soft constraints for precise boundary shaping. Our results demonstrate that SLIME achieves superior performance compared to state-of-the-art baselines while maintaining higher generation stability.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02383</guid>
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<pubDate>Mon, 02 Feb 2026 17:46:06 +0000</pubDate>
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<title>SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation</title>
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<link>https://arxiv.org/abs/2602.02402</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02402.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mu Huang, Hui Wang, Kerui Ren, Linning Xu, Yunsong Zhou, Mulin Yu, Bo Dai, Jiangmiao Pang</p><p><b>Upvotes:</b> 31</p><p><b>Summary:</b> Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control, limiting accuracy, stability, and generalization. This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation. SoMA couples deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space for end-to-end real-to-sim simulation. Modeling interactions over learned Gaussian splats enables controllable, stable long-horizon manipulation and generalization beyond observed trajectories without predefined physical models. SoMA improves resimulation accuracy and generalization on real-world robot manipulation by 20%, enabling stable simulation of complex tasks such as long-horizon cloth folding.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02402</guid>
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<pubDate>Mon, 02 Feb 2026 17:59:31 +0000</pubDate>
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<title>WideSeek: Advancing Wide Research via Multi-Agent Scaling</title>
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<link>https://arxiv.org/abs/2602.02636</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02636.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ziyang Huang, Haolin Ren, Xiaowei Yuan, Jiawei Wang, Zhongtao Jiang, Kun Xu, Shizhu He, Jun Zhao, Kang Liu</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in parallel. However, progress in this field is impeded by the lack of dedicated benchmarks and optimization methodologies for search breadth. To address these challenges, we take a deep dive into Wide Research from two perspectives: Data Pipeline and Agent Optimization. First, we produce WideSeekBench, a General Broad Information Seeking (GBIS) benchmark constructed via a rigorous multi-phase data pipeline to ensure diversity across the target information volume, logical constraints, and domains. Second, we introduce WideSeek, a dynamic hierarchical multi-agent architecture that can autonomously fork parallel sub-agents based on task requirements. Furthermore, we design a unified training framework that linearizes multi-agent trajectories and optimizes the system using end-to-end RL. Experimental results demonstrate the effectiveness of WideSeek and multi-agent RL, highlighting that scaling the number of agents is a promising direction for advancing the Wide Research paradigm.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02636</guid>
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<pubDate>Mon, 02 Feb 2026 18:32:48 +0000</pubDate>
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<title>UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing</title>
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<link>https://arxiv.org/abs/2602.02437</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02437.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dianyi Wang, Chaofan Ma, Feng Han, Size Wu, Wei Song, Yibin Wang, Zhixiong Zhang, Tianhang Wang, Siyuan Wang, Zhongyu Wei, Jiaqi Wang</p><p><b>Upvotes:</b> 75</p><p><b>Summary:</b> Unified multimodal models often struggle with complex synthesis tasks that demand deep reasoning, and typically treat text-to-image generation and image editing as isolated capabilities rather than interconnected reasoning steps. To address this, we propose UniReason, a unified framework that harmonizes these two tasks through a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach unifies generation and editing within a shared representation, mirroring the human cognitive process of planning followed by refinement. We support this framework by systematically constructing a large-scale reasoning-centric dataset (~300k samples) covering five major knowledge domains (e.g., cultural commonsense, physics, etc.) for planning, alongside an agent-generated corpus for visual self-correction. Extensive experiments demonstrate that UniReason achieves advanced performance on reasoning-intensive benchmarks such as WISE, KrisBench and UniREditBench, while maintaining superior general synthesis capabilities.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02437</guid>
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<pubDate>Mon, 02 Feb 2026 18:34:35 +0000</pubDate>
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<title>RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval</title>
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<link>https://arxiv.org/abs/2602.02444</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02444.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tyler Skow, Alexander Martin, Benjamin Van Durme, Rama Chellappa, Reno Kriz</p><p><b>Upvotes:</b> 19</p><p><b>Summary:</b> Reranking is a critical component of modern retrieval systems, which typically pair an efficient first-stage retriever with a more expressive model to refine results. While large reasoning models have driven rapid progress in text-centric reranking, reasoning-based reranking for video retrieval remains underexplored. To address this gap, we introduce RANKVIDEO, a reasoning-based reranker for video retrieval that explicitly reasons over query-video pairs using video content to assess relevance. RANKVIDEO is trained using a two-stage curriculum consisting of perception-grounded supervised fine-tuning followed by reranking training that combines pointwise, pairwise, and teacher confidence distillation objectives, and is supported by a data synthesis pipeline for constructing reasoning-intensive query-video pairs. Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework, yielding an average improvement of 31% on nDCG@10 and outperforming text-only and vision-language reranking alternatives, while more efficient.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02444</guid>
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<pubDate>Mon, 02 Feb 2026 18:40:37 +0000</pubDate>
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<title>Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling</title>
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<link>https://arxiv.org/abs/2602.02453</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02453.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Andong Chen, Wenxin Zhu, Qiuyu Ding, Yuchen Song, Muyun Yang, Tiejun Zhao</p><p><b>Upvotes:</b> 34</p><p><b>Summary:</b> Chain-of-Thought reasoning has driven large language models to extend from thinking with text to thinking with images and videos. However, different modalities still have clear limitations: static images struggle to represent temporal structure, while videos introduce substantial redundancy and computational cost. In this work, we propose Thinking with Comics, a visual reasoning paradigm that uses comics as a high information-density medium positioned between images and videos. Comics preserve temporal structure, embedded text, and narrative coherence while requiring significantly lower reasoning cost. We systematically study two reasoning paths based on comics and evaluate them on a range of reasoning tasks and long-context understanding tasks. Experimental results show that Thinking with Comics outperforms Thinking with Images on multi-step temporal and causal reasoning tasks, while remaining substantially more efficient than Thinking with Video. Further analysis indicates that different comic narrative structures and styles consistently affect performance across tasks, suggesting that comics serve as an effective intermediate visual representation for improving multimodal reasoning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02453</guid>
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<pubDate>Mon, 02 Feb 2026 18:43:57 +0000</pubDate>
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<title>SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning</title>
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<link>https://arxiv.org/abs/2602.02472</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02472.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qifan Yu, Xinyu Ma, Zhijian Zhuo, Minrui Wang, Deyi Liu, Shiyi Zhan, Yiyuan Ma, Liang Xiang, Xingyan Bin, Di He</p><p><b>Upvotes:</b> 44</p><p><b>Summary:</b> Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansion remains significantly understudied, with the few existing methods limited to the early stages of training. However, expanding width during the mid-stage is essential for maximizing computational savings, yet it remains a formidable challenge due to severe training instabilities. Empirically, we show that naive initialization at this stage disrupts activation statistics, triggering loss spikes, while copy-based initialization introduces gradient symmetry that hinders feature diversity. To address these issues, we propose SPARKLING (balancing {S}ignal {P}reservation {A}nd symmet{R}y brea{K}ing for width-progressive {L}earn{ING}), a novel framework for mid-stage width expansion. Our method achieves signal preservation via RMS-scale consistency, stabilizing activation statistics during expansion. Symmetry breaking is ensured through asymmetric optimizer state resetting and learning rate re-warmup. Extensive experiments on Mixture-of-Experts (MoE) models demonstrate that, across multiple width axes and optimizer families, SPARKLING consistently outperforms training from scratch and reduces training cost by up to 35% under 2times width expansion.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02472</guid>
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<pubDate>Mon, 02 Feb 2026 18:52:52 +0000</pubDate>
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<title>MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents</title>
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<link>https://arxiv.org/abs/2602.02474</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02474.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, Wenya Wang</p><p><b>Upvotes:</b> 46</p><p><b>Summary:</b> Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this end, we present MemSkill, which reframes these operations as learnable and evolvable memory skills, structured and reusable routines for extracting, consolidating, and pruning information from interaction traces. Inspired by the design philosophy of agent skills, MemSkill employs a controller that learns to select a small set of relevant skills, paired with an LLM-based executor that produces skill-guided memories. Beyond learning skill selection, MemSkill introduces a designer that periodically reviews hard cases where selected skills yield incorrect or incomplete memories, and evolves the skill set by proposing refinements and new skills. Together, MemSkill forms a closed-loop procedure that improves both the skill-selection policy and the skill set itself. Experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld demonstrate that MemSkill improves task performance over strong baselines and generalizes well across settings. Further analyses shed light on how skills evolve, offering insights toward more adaptive, self-evolving memory management for LLM agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02474</guid>
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<pubDate>Mon, 02 Feb 2026 18:53:28 +0000</pubDate>
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<title>RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents</title>
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<link>https://arxiv.org/abs/2602.02486</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02486.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jialiang Zhu, Gongrui Zhang, Xiaolong Ma, Lin Xu, Miaosen Zhang, Ruiqi Yang, Song Wang, Kai Qiu, Zhirong Wu, Qi Dai, Ruichun Ma, Bei Liu, Yifan Yang, Chong Luo, Zhengyuan Yang, Linjie Li, Lijuan Wang, Weizhu Chen, Xin Geng, Baining Guo</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> LLM-based deep research agents are largely built on the ReAct framework. This linear design makes it difficult to revisit earlier states, branch into alternative search directions, or maintain global awareness under long contexts, often leading to local optima, redundant exploration, and inefficient search. We propose Re-TRAC, an agentic framework that performs cross-trajectory exploration by generating a structured state representation after each trajectory to summarize evidence, uncertainties, failures, and future plans, and conditioning subsequent trajectories on this state representation. This enables iterative reflection and globally informed planning, reframing research as a progressive process. Empirical results show that Re-TRAC consistently outperforms ReAct by 15-20% on BrowseComp with frontier LLMs. For smaller models, we introduce Re-TRAC-aware supervised fine-tuning, achieving state-of-the-art performance at comparable scales. Notably, Re-TRAC shows a monotonic reduction in tool calls and token usage across rounds, indicating progressively targeted exploration driven by cross-trajectory reflection rather than redundant search.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02486</guid>
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<pubDate>Mon, 02 Feb 2026 18:58:07 +0000</pubDate>
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<title>RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System</title>
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<link>https://arxiv.org/abs/2602.02488</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02488.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yinjie Wang, Tianbao Xie, Ke Shen, Mengdi Wang, Ling Yang</p><p><b>Upvotes:</b> 30</p><p><b>Summary:</b> We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, amplifying learning signals and strengthening the overall RL system for any LLM or agentic scenarios. Specifically, the policy is trained with integrated feedback from step-wise and outcome signals, while the reward model is jointly optimized via consistency feedback, which in turn further improves policy training. Moreover, our theory-motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each, enabling learning from experience. Empirically, each added component consistently improves the overall system, and RLAnything yields substantial gains across various representative LLM and agentic tasks, boosting Qwen3-VL-8B-Thinking by 9.1% on OSWorld and Qwen2.5-7B-Instruct by 18.7% and 11.9% on AlfWorld and LiveBench, respectively. We also that optimized reward-model signals outperform outcomes that rely on human labels. Code: https://github.com/Gen-Verse/Open-AgentRL</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02488</guid>
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<pubDate>Mon, 02 Feb 2026 18:59:04 +0000</pubDate>
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<title>PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss</title>
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<link>https://arxiv.org/abs/2602.02493</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02493.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zehong Ma, Ruihan Xu, Shiliang Zhang</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> Pixel diffusion generates images directly in pixel space in an end-to-end manner, avoiding the artifacts and bottlenecks introduced by VAEs in two-stage latent diffusion. However, it is challenging to optimize high-dimensional pixel manifolds that contain many perceptually irrelevant signals, leaving existing pixel diffusion methods lagging behind latent diffusion models. We propose PixelGen, a simple pixel diffusion framework with perceptual supervision. Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful perceptual manifold. An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. With perceptual supervision, PixelGen surpasses strong latent diffusion baselines. It achieves an FID of 5.11 on ImageNet-256 without classifier-free guidance using only 80 training epochs, and demonstrates favorable scaling performance on large-scale text-to-image generation with a GenEval score of 0.79. PixelGen requires no VAEs, no latent representations, and no auxiliary stages, providing a simpler yet more powerful generative paradigm. Codes are publicly available at https://github.com/Zehong-Ma/PixelGen.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02493</guid>
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<pubDate>Mon, 02 Feb 2026 18:59:42 +0000</pubDate>
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<title>MARS: Modular Agent with Reflective Search for Automated AI Research</title>
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<link>https://arxiv.org/abs/2602.02660</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02660.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam, Rui Meng, Tomas Pfister, Jinsung Yoon</p><p><b>Upvotes:</b> 57</p><p><b>Summary:</b> Automating AI research differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often generating monolithic scripts that ignore execution costs and causal factors. We introduce MARS (Modular Agent with Reflective Search), a framework optimized for autonomous AI research. MARS relies on three pillars: (1) Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to explicitly balance performance with execution expense; (2) Modular Construction, employing a "Design-Decompose-Implement" pipeline to manage complex research repositories; and (3) Comparative Reflective Memory, which addresses credit assignment by analyzing solution differences to distill high-signal insights. MARS achieves state-of-the-art performance among open-source frameworks on MLE-Bench under comparable settings, maintaining competitiveness with the global leaderboard's top methods. Furthermore, the system exhibits qualitative "Aha!" moments, where 63% of all utilized lessons originate from cross-branch transfer, demonstrating that the agent effectively generalizes insights across search paths.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02660</guid>
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<pubDate>Mon, 02 Feb 2026 19:00:03 +0000</pubDate>
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<title>Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization</title>
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<link>https://arxiv.org/abs/2602.02958</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.02958.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haocheng Xi, Shuo Yang, Yilong Zhao, Muyang Li, Han Cai, Xingyang Li, Yujun Lin, Zhuoyang Zhang, Jintao Zhang, Xiuyu Li, Zhiying Xu, Jun Wu, Chenfeng Xu, Ion Stoica, Song Han, Kurt Keutzer</p><p><b>Upvotes:</b> 32</p><p><b>Summary:</b> Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capability: KV cache memory. In autoregressive video generation models, the KV cache grows with generation history and quickly dominates GPU memory, often exceeding 30 GB, preventing deployment on widely available hardware. More critically, constrained KV cache budgets restrict the effective working memory, directly degrading long horizon consistency in identity, layout, and motion. To address this challenge, we present Quant VideoGen (QVG), a training free KV cache quantization framework for autoregressive video diffusion models. QVG leverages video spatiotemporal redundancy through Semantic Aware Smoothing, producing low magnitude, quantization friendly residuals. It further introduces Progressive Residual Quantization, a coarse to fine multi stage scheme that reduces quantization error while enabling a smooth quality memory trade off. Across LongCat Video, HY WorldPlay, and Self Forcing benchmarks, QVG establishes a new Pareto frontier between quality and memory efficiency, reducing KV cache memory by up to 7.0 times with less than 4% end to end latency overhead while consistently outperforming existing baselines in generation quality.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.02958</guid>
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<pubDate>Tue, 03 Feb 2026 00:54:32 +0000</pubDate>
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<title>LatentMem: Customizing Latent Memory for Multi-Agent Systems</title>
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<link>https://arxiv.org/abs/2602.03036</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03036.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Muxin Fu, Guibin Zhang, Xiangyuan Xue, Yafu Li, Zefeng He, Siyuan Huang, Xiaoye Qu, Yu Cheng, Yang Yang</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization arising from the absence of role-aware customization, and (ii) information overload induced by excessively fine-grained memory entries. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework designed to customize agent-specific memories in a token-efficient manner. Specifically, LatentMem comprises an experience bank that stores raw interaction trajectories in a lightweight form, and a memory composer that synthesizes compact latent memories conditioned on retrieved experience and agent-specific contexts. Further, we introduce Latent Memory Policy Optimization (LMPO), which propagates task-level optimization signals through latent memories to the composer, encouraging it to produce compact and high-utility representations. Extensive experiments across diverse benchmarks and mainstream MAS frameworks show that LatentMem achieves a performance gain of up to 19.36% over vanilla settings and consistently outperforms existing memory architectures, without requiring any modifications to the underlying frameworks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03036</guid>
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<pubDate>Tue, 03 Feb 2026 03:03:16 +0000</pubDate>
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<title>CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs</title>
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<link>https://arxiv.org/abs/2602.03048</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03048.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhiyuan Yao, Yi-Kai Zhang, Yuxin Chen, Yueqing Sun, Zishan Xu, Yu Yang, Tianhao Hu, Qi Gu, Hui Su, Xunliang Cai</p><p><b>Upvotes:</b> 33</p><p><b>Summary:</b> Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key approach for enhancing LLM reasoning.However, standard frameworks like Group Relative Policy Optimization (GRPO) typically employ a uniform rollout budget, leading to resource inefficiency. Moreover, existing adaptive methods often rely on instance-level metrics, such as task pass rates, failing to capture the model's dynamic learning state. To address these limitations, we propose CoBA-RL, a reinforcement learning algorithm designed to adaptively allocate rollout budgets based on the model's evolving capability. Specifically, CoBA-RL utilizes a Capability-Oriented Value function to map tasks to their potential training gains and employs a heap-based greedy strategy to efficiently self-calibrate the distribution of computational resources to samples with high training value. Extensive experiments demonstrate that our approach effectively orchestrates the trade-off between exploration and exploitation, delivering consistent generalization improvements across multiple challenging benchmarks. These findings underscore that quantifying sample training value and optimizing budget allocation are pivotal for advancing LLM post-training efficiency.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03048</guid>
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<pubDate>Tue, 03 Feb 2026 03:14:36 +0000</pubDate>
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<title>Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2602.03086</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03086.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiayao Mai, Bangyan Liao, Zhenjun Zhao, Yingping Zeng, Haoang Li, Javier Civera, Tailin Wu, Yi Zhou, Peidong Liu</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> The Homotopy paradigm, a general principle for solving challenging problems, appears across diverse domains such as robust optimization, global optimization, polynomial root-finding, and sampling. Practical solvers for these problems typically follow a predictor-corrector (PC) structure, but rely on hand-crafted heuristics for step sizes and iteration termination, which are often suboptimal and task-specific. To address this, we unify these problems under a single framework, which enables the design of a general neural solver. Building on this unified view, we propose Neural Predictor-Corrector (NPC), which replaces hand-crafted heuristics with automatically learned policies. NPC formulates policy selection as a sequential decision-making problem and leverages reinforcement learning to automatically discover efficient strategies. To further enhance generalization, we introduce an amortized training mechanism, enabling one-time offline training for a class of problems and efficient online inference on new instances. Experiments on four representative homotopy problems demonstrate that our method generalizes effectively to unseen instances. It consistently outperforms classical and specialized baselines in efficiency while demonstrating superior stability across tasks, highlighting the value of unifying homotopy methods into a single neural framework.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03086</guid>
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<pubDate>Tue, 03 Feb 2026 04:19:48 +0000</pubDate>
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<title>Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis</title>
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<link>https://arxiv.org/abs/2602.03139</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03139.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tianhe Wu, Ruibin Li, Lei Zhang, Kede Ma</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> Distribution matching distillation (DMD) aligns a multi-step generator with its few-step counterpart to enable high-quality generation under low inference cost. However, DMD tends to suffer from mode collapse, as its reverse-KL formulation inherently encourages mode-seeking behavior, for which existing remedies typically rely on perceptual or adversarial regularization, thereby incurring substantial computational overhead and training instability. In this work, we propose a role-separated distillation framework that explicitly disentangles the roles of distilled steps: the first step is dedicated to preserving sample diversity via a target-prediction (e.g., v-prediction) objective, while subsequent steps focus on quality refinement under the standard DMD loss, with gradients from the DMD objective blocked at the first step. We term this approach Diversity-Preserved DMD (DP-DMD), which, despite its simplicity -- no perceptual backbone, no discriminator, no auxiliary networks, and no additional ground-truth images -- preserves sample diversity while maintaining visual quality on par with state-of-the-art methods in extensive text-to-image experiments.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03139</guid>
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<pubDate>Tue, 03 Feb 2026 05:45:25 +0000</pubDate>
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<title>Self-Hinting Language Models Enhance Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2602.03143</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03143.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Group Relative Policy Optimization (GRPO) has recently emerged as a practical recipe for aligning large language models with verifiable objectives. However, under sparse terminal rewards, GRPO often stalls because rollouts within a group frequently receive identical rewards, causing relative advantages to collapse and updates to vanish. We propose self-hint aligned GRPO with privileged supervision (SAGE), an on-policy reinforcement learning framework that injects privileged hints during training to reshape the rollout distribution under the same terminal verifier reward. For each prompt x, the model samples a compact hint h (e.g., a plan or decomposition) and then generates a solution τ conditioned on (x,h). Crucially, the task reward R(x,τ) is unchanged; hints only increase within-group outcome diversity under finite sampling, preventing GRPO advantages from collapsing under sparse rewards. At test time, we set h=varnothing and deploy the no-hint policy without any privileged information. Moreover, sampling diverse self-hints serves as an adaptive curriculum that tracks the learner's bottlenecks more effectively than fixed hints from an initial policy or a stronger external model. Experiments over 6 benchmarks with 3 LLMs show that SAGE consistently outperforms GRPO, on average +2.0 on Llama-3.2-3B-Instruct, +1.2 on Qwen2.5-7B-Instruct and +1.3 on Qwen3-4B-Instruct. The code is available at https://github.com/BaohaoLiao/SAGE.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03143</guid>
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<pubDate>Tue, 03 Feb 2026 05:56:20 +0000</pubDate>
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<title>FASA: Frequency-aware Sparse Attention</title>
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<link>https://arxiv.org/abs/2602.03152</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03152.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yifei Wang, Yueqi Wang, Zhenrui Yue, Huimin Zeng, Yong Wang, Ismini Lourentzou, Zhengzhong Tu, Xiangxiang Chu, Julian McAuley</p><p><b>Upvotes:</b> 113</p><p><b>Summary:</b> The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address this bottleneck, the token pruning paradigm leverages attention sparsity to selectively retain a small, critical subset of tokens. However, existing approaches fall short, with static methods risking irreversible information loss and dynamic strategies employing heuristics that insufficiently capture the query-dependent nature of token importance. We propose FASA, a novel framework that achieves query-aware token eviction by dynamically predicting token importance. FASA stems from a novel insight into RoPE: the discovery of functional sparsity at the frequency-chunk (FC) level. Our key finding is that a small, identifiable subset of "dominant" FCs consistently exhibits high contextual agreement with the full attention head. This provides a robust and computationally free proxy for identifying salient tokens. %making them a powerful and efficient proxy for token importance. Building on this insight, FASA first identifies a critical set of tokens using dominant FCs, and then performs focused attention computation solely on this pruned subset. % Since accessing only a small fraction of the KV cache, FASA drastically lowers memory bandwidth requirements and computational cost. Across a spectrum of long-context tasks, from sequence modeling to complex CoT reasoning, FASA consistently outperforms all token-eviction baselines and achieves near-oracle accuracy, demonstrating remarkable robustness even under constraint budgets. Notably, on LongBench-V1, FASA reaches nearly 100\% of full-KV performance when only keeping 256 tokens, and achieves 2.56times speedup using just 18.9\% of the cache on AIME24.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03152</guid>
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<pubDate>Tue, 03 Feb 2026 06:09:06 +0000</pubDate>
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<title>Accurate Failure Prediction in Agents Does Not Imply Effective Failure Prevention</title>
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<link>https://arxiv.org/abs/2602.03338</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03338.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Rakshith Vasudev, Melisa Russak, Dan Bikel, Waseem Alshikh</p><p><b>Upvotes:</b> 25</p><p><b>Summary:</b> Proactive interventions by LLM critic models are often assumed to improve reliability, yet their effects at deployment time are poorly understood. We show that a binary LLM critic with strong offline accuracy (AUROC 0.94) can nevertheless cause severe performance degradation, inducing a 26 percentage point (pp) collapse on one model while affecting another by near zero pp. This variability demonstrates that LLM critic accuracy alone is insufficient to determine whether intervention is safe. We identify a disruption-recovery tradeoff: interventions may recover failing trajectories but also disrupt trajectories that would have succeeded. Based on this insight, we propose a pre-deployment test that uses a small pilot of 50 tasks to estimate whether intervention is likely to help or harm, without requiring full deployment. Across benchmarks, the test correctly anticipates outcomes: intervention degrades performance on high-success tasks (0 to -26 pp), while yielding a modest improvement on the high-failure ALFWorld benchmark (+2.8 pp, p=0.014). The primary value of our framework is therefore identifying when not to intervene, preventing severe regressions before deployment.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03338</guid>
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<pubDate>Tue, 03 Feb 2026 10:02:50 +0000</pubDate>
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<title>SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training</title>
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<link>https://arxiv.org/abs/2602.03411</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03411.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang, Ran Le, Daixuan Cheng, Guoxin Chen, Yiwen Hu, Zongchao Chen, Wayne Xin Zhao, Yang Song, Tao Zhang, Ji-Rong Wen</p><p><b>Upvotes:</b> 34</p><p><b>Summary:</b> In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design. Starting from an open-source base model with limited initial SWE capability, SWE-Master demonstrates how systematical optimization method can elicit strong long-horizon SWE task solving abilities. We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks. Under identical experimental settings, our approach achieves a resolve rate of 61.4\% with Qwen2.5-Coder-32B, substantially outperforming existing open-source baselines. By further incorporating test-time scaling~(TTS) with LLM-based environment feedback, SWE-Master reaches 70.8\% at TTS@8, demonstrating a strong performance potential. SWE-Master provides a practical and transparent foundation for advancing reproducible research on software engineering agents. The code is available at https://github.com/RUCAIBox/SWE-Master.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03411</guid>
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<pubDate>Tue, 03 Feb 2026 11:38:48 +0000</pubDate>
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<title>SWE-World: Building Software Engineering Agents in Docker-Free Environments</title>
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<link>https://arxiv.org/abs/2602.03419</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03419.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuang Sun, Huatong Song, Lisheng Huang, Jinhao Jiang, Ran Le, Zhihao Lv, Zongchao Chen, Yiwen Hu, Wenyang Luo, Wayne Xin Zhao, Yang Song, Hongteng Xu, Tao Zhang, Ji-Rong Wen</p><p><b>Upvotes:</b> 38</p><p><b>Summary:</b> Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\% to 52.0\% via Docker-free SFT, 55.0\% with Docker-free RL, and 68.2\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03419</guid>
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<pubDate>Tue, 03 Feb 2026 11:44:39 +0000</pubDate>
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<title>A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces</title>
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<link>https://arxiv.org/abs/2602.03442</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03442.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mingxuan Du, Benfeng Xu, Chiwei Zhu, Shaohan Wang, Pengyu Wang, Xiaorui Wang, Zhendong Mao</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> Frontier language models have demonstrated strong reasoning and long-horizon tool-use capabilities. However, existing RAG systems fail to leverage these capabilities. They still rely on two paradigms: (1) designing an algorithm that retrieves passages in a single shot and concatenates them into the model's input, or (2) predefining a workflow and prompting the model to execute it step-by-step. Neither paradigm allows the model to participate in retrieval decisions, preventing efficient scaling with model improvements. In this paper, we introduce A-RAG, an Agentic RAG framework that exposes hierarchical retrieval interfaces directly to the model. A-RAG provides three retrieval tools: keyword search, semantic search, and chunk read, enabling the agent to adaptively search and retrieve information across multiple granularities. Experiments on multiple open-domain QA benchmarks show that A-RAG consistently outperforms existing approaches with comparable or lower retrieved tokens, demonstrating that A-RAG effectively leverages model capabilities and dynamically adapts to different RAG tasks. We further systematically study how A-RAG scales with model size and test-time compute. We will release our code and evaluation suite to facilitate future research. Code and evaluation suite are available at https://github.com/Ayanami0730/arag.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03442</guid>
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<pubDate>Tue, 03 Feb 2026 12:07:21 +0000</pubDate>
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<title>Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers</title>
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<link>https://arxiv.org/abs/2602.03510</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03510.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bozhou Li, Yushuo Guan, Haolin Li, Bohan Zeng, Yiyan Ji, Yue Ding, Pengfei Wan, Kun Gai, Yuanxing Zhang, Wentao Zhang</p><p><b>Upvotes:</b> 26</p><p><b>Summary:</b> Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning remains largely static and often utilizes only a single LLM layer, despite pronounced semantic hierarchy across LLM layers and non-stationary denoising dynamics over both diffusion time and network depth. To better match the dynamic process of DiT generation and thereby enhance the diffusion model's generative capability, we introduce a unified normalized convex fusion framework equipped with lightweight gates to systematically organize multi-layer LLM hidden states via time-wise, depth-wise, and joint fusion. Experiments establish Depth-wise Semantic Routing as the superior conditioning strategy, consistently improving text-image alignment and compositional generation (e.g., +9.97 on the GenAI-Bench Counting task). Conversely, we find that purely time-wise fusion can paradoxically degrade visual generation fidelity. We attribute this to a train-inference trajectory mismatch: under classifier-free guidance, nominal timesteps fail to track the effective SNR, causing semantically mistimed feature injection during inference. Overall, our results position depth-wise routing as a strong and effective baseline and highlight the critical need for trajectory-aware signals to enable robust time-dependent conditioning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03510</guid>
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<pubDate>Tue, 03 Feb 2026 13:30:13 +0000</pubDate>
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<title>HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing</title>
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<link>https://arxiv.org/abs/2602.03560</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03560.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yizhao Gao, Jianyu Wei, Qihao Zhang, Yu Cheng, Shimao Chen, Zhengju Tang, Zihan Jiang, Yifan Song, Hailin Zhang, Liang Zhao, Bo Yang, Gang Wang, Shijie Cao, Fuli Luo</p><p><b>Upvotes:</b> 41</p><p><b>Summary:</b> This work introduces Hybrid Sparse Attention (HySparse), a new architecture that interleaves each full attention layer with several sparse attention layers. While conceptually simple, HySparse strategically derives each sparse layer's token selection and KV caches directly from the preceding full attention layer. This architecture resolves two fundamental limitations of prior sparse attention methods. First, conventional approaches typically rely on additional proxies to predict token importance, introducing extra complexity and potentially suboptimal performance. In contrast, HySparse uses the full attention layer as a precise oracle to identify important tokens. Second, existing sparse attention designs often reduce computation without saving KV cache. HySparse enables sparse attention layers to reuse the full attention KV cache, thereby reducing both computation and memory. We evaluate HySparse on both 7B dense and 80B MoE models. Across all settings, HySparse consistently outperforms both full attention and hybrid SWA baselines. Notably, in the 80B MoE model with 49 total layers, only 5 layers employ full attention, yet HySparse achieves substantial performance gains while reducing KV cache storage by nearly 10x.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03560</guid>
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<pubDate>Tue, 03 Feb 2026 14:05:57 +0000</pubDate>
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<title>HY3D-Bench: Generation of 3D Assets</title>
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<link>https://arxiv.org/abs/2602.03907</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03907.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Team Hunyuan3D, Bowen Zhang, Chunchao Guo, Dongyuan Guo, Haolin Liu, Hongyu Yan, Huiwen Shi, Jiaao Yu, Jiachen Xu, Jingwei Huang, Kunhong Li, Lifu Wang, Linus, Penghao Wang, Qingxiang Lin, Ruining Tang, Xianghui Yang, Yang Li, Yirui Guan, Yunfei Zhao, Yunhan Yang, Zeqiang Lai, Zhihao Liang, Zibo Zhao</p><p><b>Upvotes:</b> 22</p><p><b>Summary:</b> While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlenecks. To address this, we introduce HY3D-Bench, an open-source ecosystem designed to establish a unified, high-quality foundation for 3D generation. Our contributions are threefold: (1) We curate a library of 250k high-fidelity 3D objects distilled from large-scale repositories, employing a rigorous pipeline to deliver training-ready artifacts, including watertight meshes and multi-view renderings; (2) We introduce structured part-level decomposition, providing the granularity essential for fine-grained perception and controllable editing; and (3) We bridge real-world distribution gaps via a scalable AIGC synthesis pipeline, contributing 125k synthetic assets to enhance diversity in long-tail categories. Validated empirically through the training of Hunyuan3D-2.1-Small, HY3D-Bench democratizes access to robust data resources, aiming to catalyze innovation across 3D perception, robotics, and digital content creation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03907</guid>
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<pubDate>Tue, 03 Feb 2026 14:13:09 +0000</pubDate>
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<title>Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation</title>
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<link>https://arxiv.org/abs/2602.03619</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03619.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Changze Lv, Jie Zhou, Wentao Zhao, Jingwen Xu, Zisu Huang, Muzhao Tian, Shihan Dou, Tao Gui, Le Tian, Xiao Zhou, Xiaoqing Zheng, Xuanjing Huang, Jie Zhou</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Nowadays, training and evaluating DeepResearch-generated reports remain challenging due to the lack of verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity, or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train human-preference-aligned query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining human preference supervision and LLM-based rubric evaluation. To better handle long-horizon reasoning, we further introduce a Multi-agent Markov-state (MaMs) workflow for report generation. We empirically show that our proposed rubric generators deliver more discriminative and better human-aligned supervision than existing rubric design strategies. Moreover, when integrated into the MaMs training framework, DeepResearch systems equipped with our rubric generators consistently outperform all open-source baselines on the DeepResearch Bench and achieve performance comparable to that of leading closed-source models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03619</guid>
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<pubDate>Tue, 03 Feb 2026 15:09:56 +0000</pubDate>
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<title>AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration</title>
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<link>https://arxiv.org/abs/2602.03786</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03786.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jianhao Ruan, Zhihao Xu, Yiran Peng, Fashen Ren, Zhaoyang Yu, Xinbing Liang, Jinyu Xiang, Bang Liu, Chenglin Wu, Yuyu Luo, Jiayi Zhang</p><p><b>Upvotes:</b> 82</p><p><b>Summary:</b> Language agents have shown strong promise for task automation. Realizing this promise for increasingly complex, long-horizon tasks has driven the rise of a sub-agent-as-tools paradigm for multi-turn task solving. However, existing designs still lack a dynamic abstraction view of sub-agents, thereby hurting adaptability. We address this challenge with a unified, framework-agnostic agent abstraction that models any agent as a tuple Instruction, Context, Tools, Model. This tuple acts as a compositional recipe for capabilities, enabling the system to spawn specialized executors for each task on demand. Building on this abstraction, we introduce an agentic system AOrchestra, where the central orchestrator concretizes the tuple at each step: it curates task-relevant context, selects tools and models, and delegates execution via on-the-fly automatic agent creation. Such designs enable reducing human engineering efforts, and remain framework-agnostic with plug-and-play support for diverse agents as task executors. It also enables a controllable performance-cost trade-off, allowing the system to approach Pareto-efficient. Across three challenging benchmarks (GAIA, SWE-Bench, Terminal-Bench), AOrchestra achieves 16.28% relative improvement against the strongest baseline when paired with Gemini-3-Flash. The code is available at: https://github.com/FoundationAgents/AOrchestra</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03786</guid>
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<pubDate>Tue, 03 Feb 2026 17:46:16 +0000</pubDate>
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<title>3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation</title>
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<link>https://arxiv.org/abs/2602.03796</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03796.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhixue Fang, Xu He, Songlin Tang, Haoxian Zhang, Qingfeng Li, Xiaoqiang Liu, Pengfei Wan, Kun Gai</p><p><b>Upvotes:</b> 52</p><p><b>Summary:</b> Existing methods for human motion control in video generation typically rely on either 2D poses or explicit 3D parametric models (e.g., SMPL) as control signals. However, 2D poses rigidly bind motion to the driving viewpoint, precluding novel-view synthesis. Explicit 3D models, though structurally informative, suffer from inherent inaccuracies (e.g., depth ambiguity and inaccurate dynamics) which, when used as a strong constraint, override the powerful intrinsic 3D awareness of large-scale video generators. In this work, we revisit motion control from a 3D-aware perspective, advocating for an implicit, view-agnostic motion representation that naturally aligns with the generator's spatial priors rather than depending on externally reconstructed constraints. We introduce 3DiMo, which jointly trains a motion encoder with a pretrained video generator to distill driving frames into compact, view-agnostic motion tokens, injected semantically via cross-attention. To foster 3D awareness, we train with view-rich supervision (i.e., single-view, multi-view, and moving-camera videos), forcing motion consistency across diverse viewpoints. Additionally, we use auxiliary geometric supervision that leverages SMPL only for early initialization and is annealed to zero, enabling the model to transition from external 3D guidance to learning genuine 3D spatial motion understanding from the data and the generator's priors. Experiments confirm that 3DiMo faithfully reproduces driving motions with flexible, text-driven camera control, significantly surpassing existing methods in both motion fidelity and visual quality.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03796</guid>
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<pubDate>Tue, 03 Feb 2026 17:59:09 +0000</pubDate>
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<title>Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing</title>
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<link>https://arxiv.org/abs/2602.03845</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03845.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tong Zheng, Chengsong Huang, Runpeng Dai, Yun He, Rui Liu, Xin Ni, Huiwen Bao, Kaishen Wang, Hongtu Zhu, Jiaxin Huang, Furong Huang, Heng Huang</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probing, an interface that exposes the width-depth dynamics of parallel thinking by periodically eliciting intermediate answers from all branches. Our analysis reveals three key insights: non-monotonic scaling across width-depth allocations, heterogeneous reasoning branch lengths, and early stabilization of global consensus. Guided by these insights, we introduce Parallel-Probe, a training-free controller designed to optimize online parallel thinking. Parallel-Probe employs consensus-based early stopping to regulate reasoning depth and deviation-based branch pruning to dynamically adjust width. Extensive experiments across three benchmarks and multiple models demonstrate that Parallel-Probe establishes a superior Pareto frontier for test-time scaling. Compared to standard majority voting, it reduces sequential tokens by up to 35.8% and total token cost by over 25.8% while maintaining competitive accuracy.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03845</guid>
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<pubDate>Tue, 03 Feb 2026 18:59:41 +0000</pubDate>
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<title>VLS: Steering Pretrained Robot Policies via Vision-Language Models</title>
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<link>https://arxiv.org/abs/2602.03973</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.03973.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuo Liu, Ishneet Sukhvinder Singh, Yiqing Xu, Jiafei Duan, Ranjay Krishna</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing motor skills; instead, they expose a limitation of imitation learning under train-test shifts, where action generation is tightly coupled to training-specific spatial configurations and task specifications. Retraining or fine-tuning to address these failures is costly and conceptually misaligned, as the required behaviors already exist but cannot be selectively adapted at test time. We propose Vision-Language Steering (VLS), a training-free framework for inference-time adaptation of frozen generative robot policies. VLS treats adaptation as an inference-time control problem, steering the sampling process of a pretrained diffusion or flow-matching policy in response to out-of-distribution observation-language inputs without modifying policy parameters. By leveraging vision-language models to synthesize trajectory-differentiable reward functions, VLS guides denoising toward action trajectories that satisfy test-time spatial and task requirements. Across simulation and real-world evaluations, VLS consistently outperforms prior steering methods, achieving a 31% improvement on CALVIN and a 13% gain on LIBERO-PRO. Real-world deployment on a Franka robot further demonstrates robust inference-time adaptation under test-time spatial and semantic shifts. Project page: https://vision-language-steering.github.io/webpage/</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.03973</guid>
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<pubDate>Tue, 03 Feb 2026 19:50:16 +0000</pubDate>
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<title>Training Data Efficiency in Multimodal Process Reward Models</title>
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<link>https://arxiv.org/abs/2602.04145</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04145.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jinyuan Li, Chengsong Huang, Langlin Huang, Shaoyang Xu, Haolin Liu, Wenxuan Zhang, Jiaxin Huang</p><p><b>Upvotes:</b> 74</p><p><b>Summary:</b> Multimodal Process Reward Models (MPRMs) are central to step-level supervision for visual reasoning in MLLMs. Training MPRMs typically requires large-scale Monte Carlo (MC)-annotated corpora, incurring substantial training cost. This paper studies the data efficiency for MPRM training.Our preliminary experiments reveal that MPRM training quickly saturates under random subsampling of the training data, indicating substantial redundancy within existing MC-annotated corpora.To explain this, we formalize a theoretical framework and reveal that informative gradient updates depend on two factors: label mixtures of positive/negative steps and label reliability (average MC scores of positive steps). Guided by these insights, we propose the Balanced-Information Score (BIS), which prioritizes both mixture and reliability based on existing MC signals at the rollout level, without incurring any additional cost. Across two backbones (InternVL2.5-8B and Qwen2.5-VL-7B) on VisualProcessBench, BIS-selected subsets consistently match and even surpass the full-data performance at small fractions. Notably, the BIS subset reaches full-data performance using only 10% of the training data, improving over random subsampling by a relative 4.1%.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04145</guid>
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<pubDate>Wed, 04 Feb 2026 02:27:38 +0000</pubDate>
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<title>Steering LLMs via Scalable Interactive Oversight</title>
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<link>https://arxiv.org/abs/2602.04210</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04210.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Enyu Zhou, Zhiheng Xi, Long Ma, Zhihao Zhang, Shihan Dou, Zhikai Lei, Guoteng Wang, Rui Zheng, Hang Yan, Tao Gui, Qi Zhang, Xuanjing Huang</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> As Large Language Models increasingly automate complex, long-horizon tasks such as vibe coding, a supervision gap has emerged. While models excel at execution, users often struggle to guide them effectively due to insufficient domain expertise, the difficulty of articulating precise intent, and the inability to reliably validate complex outputs. It presents a critical challenge in scalable oversight: enabling humans to responsibly steer AI systems on tasks that surpass their own ability to specify or verify. To tackle this, we propose Scalable Interactive Oversight, a framework that decomposes complex intent into a recursive tree of manageable decisions to amplify human supervision. Rather than relying on open-ended prompting, our system elicits low-burden feedback at each node and recursively aggregates these signals into precise global guidance. Validated in web development task, our framework enables non-experts to produce expert-level Product Requirement Documents, achieving a 54\% improvement in alignment. Crucially, we demonstrate that this framework can be optimized via Reinforcement Learning using only online user feedback, offering a practical pathway for maintaining human control as AI scales.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04210</guid>
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<pubDate>Wed, 04 Feb 2026 04:52:00 +0000</pubDate>
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<title>EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models</title>
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<link>https://arxiv.org/abs/2602.04515</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04515.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Bai, MingMing Yu, Chaojie Li, Ziyi Bai, Xinlong Wang, Börje F. Karlsson</p><p><b>Upvotes:</b> 35</p><p><b>Summary:</b> Deploying humanoid robots in real-world settings is fundamentally challenging, as it demands tight integration of perception, locomotion, and manipulation under partial-information observations and dynamically changing environments. As well as transitioning robustly between sub-tasks of different types. Towards addressing these challenges, we propose a novel task - EgoActing, which requires directly grounding high-level instructions into various, precise, spatially aware humanoid actions. We further instantiate this task by introducing EgoActor, a unified and scalable vision-language model (VLM) that can predict locomotion primitives (e.g., walk, turn, move sideways, change height), head movements, manipulation commands, and human-robot interactions to coordinate perception and execution in real-time. We leverage broad supervision over egocentric RGB-only data from real-world demonstrations, spatial reasoning question-answering, and simulated environment demonstrations, enabling EgoActor to make robust, context-aware decisions and perform fluent action inference (under 1s) with both 8B and 4B parameter models. Extensive evaluations in both simulated and real-world environments demonstrate that EgoActor effectively bridges abstract task planning and concrete motor execution, while generalizing across diverse tasks and unseen environments.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04515</guid>
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<pubDate>Wed, 04 Feb 2026 13:04:56 +0000</pubDate>
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<title>Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration</title>
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<link>https://arxiv.org/abs/2602.04575</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04575.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiaheng Liu, Yuanxing Zhang, Shihao Li, Xinping Lei</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> For the past decade, the trajectory of generative artificial intelligence (AI) has been dominated by a model-centric paradigm driven by scaling laws. Despite significant leaps in visual fidelity, this approach has encountered a ``usability ceiling'' manifested as the Intent-Execution Gap (i.e., the fundamental disparity between a creator's high-level intent and the stochastic, black-box nature of current single-shot models). In this paper, inspired by the Vibe Coding, we introduce the Vibe AIGC, a new paradigm for content generation via agentic orchestration, which represents the autonomous synthesis of hierarchical multi-agent workflows. Under this paradigm, the user's role transcends traditional prompt engineering, evolving into a Commander who provides a Vibe, a high-level representation encompassing aesthetic preferences, functional logic, and etc. A centralized Meta-Planner then functions as a system architect, deconstructing this ``Vibe'' into executable, verifiable, and adaptive agentic pipelines. By transitioning from stochastic inference to logical orchestration, Vibe AIGC bridges the gap between human imagination and machine execution. We contend that this shift will redefine the human-AI collaborative economy, transforming AI from a fragile inference engine into a robust system-level engineering partner that democratizes the creation of complex, long-horizon digital assets.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04575</guid>
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<pubDate>Wed, 04 Feb 2026 14:01:44 +0000</pubDate>
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<title>WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2602.04634</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04634.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zelai Xu, Zhexuan Xu, Ruize Zhang, Chunyang Zhu, Shi Yu, Weilin Liu, Quanlu Zhang, Wenbo Ding, Chao Yu, Yu Wang</p><p><b>Upvotes:</b> 87</p><p><b>Summary:</b> Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks. Extensive experiments show that WideSeek-R1-4B achieves an item F1 score of 40.0% on the WideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04634</guid>
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<pubDate>Wed, 04 Feb 2026 15:05:12 +0000</pubDate>
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</item>
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<title>ERNIE 5.0 Technical Report</title>
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<link>https://arxiv.org/abs/2602.04705</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04705.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haifeng Wang, Hua Wu, Tian Wu, Yu Sun, Jing Liu, Dianhai Yu, Yanjun Ma, Jingzhou He, Zhongjun He, Dou Hong, Qiwen Liu, Shuohuan Wang, Junyuan Shang, Zhenyu Zhang, Yuchen Ding, Jinle Zeng, Jiabin Yang, Liang Shen, Ruibiao Chen, Weichong Yin, Siyu Ding, Dai Dai, Shikun Feng, Siqi Bao, Bolei He, Yan Chen, Zhenyu Jiao, Ruiqing Zhang, Zeyu Chen, Qingqing Dang, Kaipeng Deng, Jiajun Jiang, Enlei Gong, Guoxia Wang, Yanlin Sha, Yi Liu, Yehan Zheng, Weijian Xu, Jiaxiang Liu, Zengfeng Zeng, Yingqi Qu, Zhongli Li, Zhengkun Zhang, Xiyang Wang, Zixiang Xu, Xinchao Xu, Zhengjie Huang, Dong Wang, Bingjin Chen, Yue Chang, Xing Yuan, Shiwei Huang, Qiao Zhao, Xinzhe Ding, Shuangshuang Qiao, Baoshan Yang, Bihong Tang, Bin Li, Bingquan Wang, Binhan Tang, Binxiong Zheng, Bo Cui, Bo Ke, Bo Zhang, Bowen Zhang, Boyan Zhang, Boyang Liu, Caiji Zhang, Can Li, Chang Xu, Chao Pang, Chao Zhang, Chaoyi Yuan, Chen Chen, Cheng Cui, Chenlin Yin, Chun Gan, Chunguang Chai, Chuyu Fang, Cuiyun Han, Dan Zhang, Danlei Feng, Danxiang Zhu, Dong Sun, Dongbo Li, Dongdong Li, Dongdong Liu, Dongxue Liu, Fan Ding, Fan Hu, Fan Li, Fan Mo, Feisheng Wu, Fengwei Liu, Gangqiang Hu, Gaofeng Lu, Gaopeng Yong, Gexiao Tian, Guan Wang, Guangchen Ni, Guangshuo Wu, Guanzhong Wang, Guihua Liu, Guishun Li, Haibin Li, Haijian Liang, Haipeng Ming, Haisu Wang, Haiyang Lu, Haiye Lin, Han Zhou, Hangting Lou, Hanwen Du, Hanzhi Zhang, Hao Chen, Hao Du, Hao Liu, Hao Zhou, Haochen Jiang, Haodong Tian, Haoshuang Wang, Haozhe Geng, Heju Yin, Hong Chen, Hongchen Xue, Hongen Liu, Honggeng Zhang, Hongji Xu, Hongwei Chen, Hongyang Zhang, Hongyuan Zhang, Hua Lu, Huan Chen, Huan Wang, Huang He, Hui Liu, Hui Zhong, Huibin Ruan, Jiafeng Lu, Jiage Liang, Jiahao Hu, Jiahao Hu, Jiajie Yang, Jialin Li, Jian Chen, Jian Wu, Jianfeng Yang, Jianguang Jiang, Jianhua Wang, Jianye Chen, Jiaodi Liu, Jiarui Zhou, Jiawei Lv, Jiaxin Zhou, Jiaxuan Liu, Jie Han, Jie Sun, Jiefan Fang, Jihan Liu, Jihua Liu, Jing Hu, Jing Qian, Jing Yan, Jingdong Du, Jingdong Wang, Jingjing Wu, Jingyong Li, Jinheng Wang, Jinjin Li, Jinliang Lu, Jinlin Yu, Jinnan Liu, Jixiang Feng, Jiyi Huang, Jiyuan Zhang, Jun Liang, Jun Xia, Jun Yu, Junda Chen, Junhao Feng, Junhong Xiang, Junliang Li, Kai Liu, Kailun Chen, Kairan Su, Kang Hu, Kangkang Zhou, Ke Chen, Ke Wei, Kui Huang, Kun Wu, Kunbin Chen, Lei Han, Lei Sun, Lei Wen, Linghui Meng, Linhao Yu, Liping Ouyang, Liwen Zhang, Longbin Ji, Longzhi Wang, Meng Sun, Meng Tian, Mengfei Li, Mengqi Zeng, Mengyu Zhang, Ming Hong, Mingcheng Zhou, Mingming Huang, Mingxin Chen, Mingzhu Cai, Naibin Gu, Nemin Qiu, Nian Wang, Peng Qiu, Peng Zhao, Pengyu Zou, Qi Wang, Qi Xin, Qian Wang, Qiang Zhu, Qianhui Luo, Qianwei Yang, Qianyue He, Qifei Wu, Qinrui Li, Qiwen Bao, Quan Zhang, Quanxiang Liu, Qunyi Xie, Rongrui Zhan, Rufeng Dai, Rui Peng, Ruian Liu, Ruihao Xu, Ruijie Wang, Ruixi Zhang, Ruixuan Liu, Runsheng Shi, Ruting Wang, Senbo Kang, Shan Lu, Shaofei Yu, Shaotian Gong, Shenwei Hu, Shifeng Zheng, Shihao Guo, Shilong Fan, Shiqin Liu, Shiwei Gu, Shixi Zhang, Shuai Yao, Shuang Zhang, Shuangqiao Liu, Shuhao Liang, Shuwei He, Shuwen Yang, Sijun He, Siming Dai, Siming Wu, Siyi Long, Songhe Deng, Suhui Dong, Suyin Liang, Teng Hu, Tianchan Xu, Tianliang Lv, Tianmeng Yang, Tianyi Wei, Tiezhu Gao, Ting Sun, Ting Zhang, Tingdan Luo, Wei He, Wei Luan, Wei Yin, Wei Zhang, Wei Zhou, Weibao Gong, Weibin Li, Weicheng Huang, Weichong Dang, Weiguo Zhu, Weilong Zhang, Weiqi Tan, Wen Huang, Wenbin Chang, Wenjing Du, Wenlong Miao, Wenpei Luo, Wenquan Wu, Xi Shi, Xi Zhao, Xiang Gao, Xiangguo Zhang, Xiangrui Yu, Xiangsen Wang, Xiangzhe Wang, Xianlong Luo, Xianying Ma, Xiao Tan, Xiaocong Lin, Xiaofei Wang, Xiaofeng Peng, Xiaofeng Wu, Xiaojian Xu, Xiaolan Yuan, Xiaopeng Cui, Xiaotian Han, Xiaoxiong Liu, Xiaoxu Fei, Xiaoxuan Wu, Xiaoyu Wang, Xiaoyu Zhang, Xin Sun, Xin Wang, Xinhui Huang, Xinming Zhu, Xintong Yu, Xinyi Xu, Xinyu Wang, Xiuxian Li, XuanShi Zhu, Xue Xu, Xueying Lv, Xuhong Li, Xulong Wei, Xuyi Chen, Yabing Shi, Yafeng Wang, Yamei Li, Yan Liu, Yanfu Cheng, Yang Gao, Yang Liang, Yang Wang, Yang Wang, Yang Yang, Yanlong Liu, Yannian Fu, Yanpeng Wang, Yanzheng Lin, Yao Chen, Yaozong Shen, Yaqian Han, Yehua Yang, Yekun Chai, Yesong Wang, Yi Song, Yichen Zhang, Yifei Wang, Yifeng Guo, Yifeng Kou, Yilong Chen, Yilong Guo, Yiming Wang, Ying Chen, Ying Wang, Yingsheng Wu, Yingzhan Lin, Yinqi Yang, Yiran Xing, Yishu Lei, Yixiang Tu, Yiyan Chen, Yong Zhang, Yonghua Li, Yongqiang Ma, Yongxing Dai, Yongyue Zhang, Yu Ran, Yu Sun, Yu-Wen Michael Zhang, Yuang Liu, Yuanle Liu, Yuanyuan Zhou, Yubo Zhang, Yuchen Han, Yucheng Wang, Yude Gao, Yuedong Luo, Yuehu Dong, Yufeng Hu, Yuhui Cao, Yuhui Yun, Yukun Chen, Yukun Gao, Yukun Li, Yumeng Zhang, Yun Fan, Yun Ma, Yunfei Zhang, Yunshen Xie, Yuping Xu, Yuqin Zhang, Yuqing Liu, Yurui Li, Yuwen Wang, Yuxiang Lu, Zefeng Cai, Zelin Zhao, Zelun Zhang, Zenan Lin, Zezhao Dong, Zhaowu Pan, Zhaoyu Liu, Zhe Dong, Zhe Zhang, Zhen Zhang, Zhengfan Wu, Zhengrui Wei, Zhengsheng Ning, Zhenxing Li, Zhenyu Li, Zhenyu Qian, Zhenyun Li, Zhi Li, Zhichao Chen, Zhicheng Dong, Zhida Feng, Zhifan Feng, Zhihao Deng, Zhijin Yu, Zhiyang Chen, Zhonghui Zheng, Zhuangzhuang Guo, Zhujun Zhang, Zhuo Sun, Zichang Liu, Zihan Lin, Zihao Huang, Zihe Zhu, Ziheng Zhao, Ziping Chen, Zixuan Zhu, Ziyang Xu, Ziyi Liang, Ziyuan Gao</p><p><b>Upvotes:</b> 239</p><p><b>Summary:</b> In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio. All modalities are trained from scratch under a unified next-group-of-tokens prediction objective, based on an ultra-sparse mixture-of-experts (MoE) architecture with modality-agnostic expert routing. To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm. Within a single pre-training run, the model learns a family of sub-models with varying depths, expert capacities, and routing sparsity, enabling flexible trade-offs among performance, model size, and inference latency in memory- or time-constrained scenarios. Moreover, we systematically address the challenges of scaling reinforcement learning to unified foundation models, thereby guaranteeing efficient and stable post-training under ultra-sparse MoE architectures and diverse multimodal settings. Extensive experiments demonstrate that ERNIE 5.0 achieves strong and balanced performance across multiple modalities. To the best of our knowledge, among publicly disclosed models, ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation. To facilitate further research, we present detailed visualizations of modality-agnostic expert routing in the unified model, alongside comprehensive empirical analysis of elastic training, aiming to offer profound insights to the community.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04705</guid>
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<pubDate>Wed, 04 Feb 2026 16:18:15 +0000</pubDate>
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<title>From Data to Behavior: Predicting Unintended Model Behaviors Before Training</title>
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<link>https://arxiv.org/abs/2602.04735</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04735.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mengru Wang, Zhenqian Xu, Junfeng Fang, Yunzhi Yao, Shumin Deng, Huajun Chen, Ningyu Zhang</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Large Language Models (LLMs) can acquire unintended biases from seemingly benign training data even without explicit cues or malicious content. Existing methods struggle to detect such risks before fine-tuning, making post hoc evaluation costly and inefficient. To address this challenge, we introduce Data2Behavior, a new task for predicting unintended model behaviors prior to training. We also propose Manipulating Data Features (MDF), a lightweight approach that summarizes candidate data through their mean representations and injects them into the forward pass of a base model, allowing latent statistical signals in the data to shape model activations and reveal potential biases and safety risks without updating any parameters. MDF achieves reliable prediction while consuming only about 20% of the GPU resources required for fine-tuning. Experiments on Qwen3-14B, Qwen2.5-32B-Instruct, and Gemma-3-12b-it confirm that MDF can anticipate unintended behaviors and provide insight into pre-training vulnerabilities.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04735</guid>
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<pubDate>Wed, 04 Feb 2026 16:37:17 +0000</pubDate>
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<title>OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models</title>
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<link>https://arxiv.org/abs/2602.04804</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04804.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yue Ding, Yiyan Ji, Jungang Li, Xuyang Liu, Xinlong Chen, Junfei Wu, Bozhou Li, Bohan Zeng, Yang Shi, Yushuo Guan, Yuanxing Zhang, Jiaheng Liu, Qiang Liu, Pengfei Wan, Liang Wang</p><p><b>Upvotes:</b> 44</p><p><b>Summary:</b> Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences leads to substantial computational overhead. Despite this challenge, token compression methods designed for Omni-LLMs remain limited. To bridge this gap, we propose OmniSIFT (Omni-modal Spatio-temporal Informed Fine-grained Token compression), a modality-asymmetric token compression framework tailored for Omni-LLMs. Specifically, OmniSIFT adopts a two-stage compression strategy: (i) a spatio-temporal video pruning module that removes video redundancy arising from both intra-frame structure and inter-frame overlap, and (ii) a vision-guided audio selection module that filters audio tokens. The entire framework is optimized end-to-end via a differentiable straight-through estimator. Extensive experiments on five representative benchmarks demonstrate the efficacy and robustness of OmniSIFT. Notably, for Qwen2.5-Omni-7B, OmniSIFT introduces only 4.85M parameters while maintaining lower latency than training-free baselines such as OmniZip. With merely 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the performance of the full-token model on several tasks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04804</guid>
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<pubDate>Wed, 04 Feb 2026 17:51:05 +0000</pubDate>
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<title>Horizon-LM: A RAM-Centric Architecture for LLM Training</title>
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<link>https://arxiv.org/abs/2602.04816</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04816.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhengqing Yuan, Lichao Sun, Yanfang, Ye</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> The rapid growth of large language models (LLMs) has outpaced the evolution of single-GPU hardware, making model scale increasingly constrained by memory capacity rather than computation. While modern training systems extend GPU memory through distributed parallelism and offloading across CPU and storage tiers, they fundamentally retain a GPU-centric execution paradigm in which GPUs host persistent model replicas and full autograd graphs. As a result, scaling large models remains tightly coupled to multi-GPU clusters, complex distributed runtimes, and unpredictable host memory consumption, creating substantial barriers for node-scale post-training workloads such as instruction tuning, alignment, and domain adaptation. We present Horizon-LM, a memory-centric training system that redefines the roles of CPU and GPU for large-model optimization. Horizon-LM treats host memory as the authoritative parameter store and uses GPUs solely as transient compute engines through a CPU-master, GPU-template execution model. By eliminating persistent GPU-resident modules and autograd graphs, employing explicit recomputation with manual gradient propagation, and introducing a pipelined double-buffered execution engine, Horizon-LM decouples model scale from GPU count and bounds memory usage to the theoretical parameter footprint. On a single H200 GPU with 1.5\,TB host RAM, Horizon-LM reliably trains models up to 120B parameters. On a standard single A100 machine, Horizon-LM achieves up to 12.2times higher training throughput than DeepSpeed ZeRO-3 with CPU offloading while preserving numerical correctness. Across platforms and scales, Horizon-LM sustains high device utilization and predictable memory growth, demonstrating that host memory, not GPU memory, defines the true feasibility boundary for node-scale large-model training.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04816</guid>
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<pubDate>Wed, 04 Feb 2026 18:04:46 +0000</pubDate>
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<title>Privileged Information Distillation for Language Models</title>
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<link>https://arxiv.org/abs/2602.04942</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04942.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Emiliano Penaloza, Dheeraj Vattikonda, Nicolas Gontier, Alexandre Lacoste, Laurent Charlin, Massimo Caccia</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Training-time privileged information (PI) can enable language models to succeed on tasks they would otherwise fail, making it a powerful tool for reinforcement learning in hard, long-horizon settings. However, transferring capabilities learned with PI to policies that must act without it at inference time remains a fundamental challenge. We study this problem in the context of distilling frontier models for multi-turn agentic environments, where closed-source systems typically hide their internal reasoning and expose only action trajectories. This breaks standard distillation pipelines, since successful behavior is observable but the reasoning process is not. For this, we introduce π-Distill, a joint teacher-student objective that trains a PI-conditioned teacher and an unconditioned student simultaneously using the same model. Additionally, we also introduce On-Policy Self-Distillation (OPSD), an alternative approach that trains using Reinforcement Learning (RL) with a reverse KL-penalty between the student and the PI-conditioned teacher. We show that both of these algorithms effectively distill frontier agents using action-only PI. Specifically we find that π-Distill and in some cases OPSD, outperform industry standard practices (Supervised finetuning followed by RL) that assume access to full Chain-of-Thought supervision across multiple agentic benchmarks, models, and forms of PI. We complement our results with extensive analysis that characterizes the factors enabling effective learning with PI, focusing primarily on π-Distill and characterizing when OPSD is competitive.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04942</guid>
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<pubDate>Wed, 04 Feb 2026 18:46:17 +0000</pubDate>
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<title>Rethinking the Trust Region in LLM Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2602.04879</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04879.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Penghui Qi, Xiangxin Zhou, Zichen Liu, Tianyu Pang, Chao Du, Min Lin, Wee Sun Lee</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm. Despite its ubiquity, we argue that the core ratio clipping mechanism in PPO is structurally ill-suited for the large vocabularies inherent to LLMs. PPO constrains policy updates based on the probability ratio of sampled tokens, which serves as a noisy single-sample Monte Carlo estimate of the true policy divergence. This creates a sub-optimal learning dynamic: updates to low-probability tokens are aggressively over-penalized, while potentially catastrophic shifts in high-probability tokens are under-constrained, leading to training inefficiency and instability. To address this, we propose Divergence Proximal Policy Optimization (DPPO), which substitutes heuristic clipping with a more principled constraint based on a direct estimate of policy divergence (e.g., Total Variation or KL). To avoid huge memory footprint, we introduce the efficient Binary and Top-K approximations to capture the essential divergence with negligible overhead. Extensive empirical evaluations demonstrate that DPPO achieves superior training stability and efficiency compared to existing methods, offering a more robust foundation for RL-based LLM fine-tuning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04879</guid>
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<pubDate>Wed, 04 Feb 2026 18:59:04 +0000</pubDate>
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<title>Reinforced Attention Learning</title>
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<link>https://arxiv.org/abs/2602.04884</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.04884.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bangzheng Li, Jianmo Ni, Chen Qu, Ian Miao, Liu Yang, Xingyu Fu, Muhao Chen, Derek Zhiyuan Cheng</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multimodal LLMs (MLLMs) through verbose rationales yields limited gains for perception and can even degrade performance. We propose Reinforced Attention Learning (RAL), a policy-gradient framework that directly optimizes internal attention distributions rather than output token sequences. By shifting optimization from what to generate to where to attend, RAL promotes effective information allocation and improved grounding in complex multimodal inputs. Experiments across diverse image and video benchmarks show consistent gains over GRPO and other baselines. We further introduce On-Policy Attention Distillation, demonstrating that transferring latent attention behaviors yields stronger cross-modal alignment than standard knowledge distillation. Our results position attention policies as a principled and general alternative for multimodal post-training.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.04884</guid>
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<pubDate>Wed, 04 Feb 2026 18:59:52 +0000</pubDate>
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<title>Semantic Search over 9 Million Mathematical Theorems</title>
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<link>https://arxiv.org/abs/2602.05216</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05216.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Luke Alexander, Eric Leonen, Sophie Szeto, Artemii Remizov, Ignacio Tejeda, Giovanni Inchiostro, Vasily Ilin</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Searching for mathematical results remains difficult: most existing tools retrieve entire papers, while mathematicians and theorem-proving agents often seek a specific theorem, lemma, or proposition that answers a query. While semantic search has seen rapid progress, its behavior on large, highly technical corpora such as research-level mathematical theorems remains poorly understood. In this work, we introduce and study semantic theorem retrieval at scale over a unified corpus of 9.2 million theorem statements extracted from arXiv and seven other sources, representing the largest publicly available corpus of human-authored, research-level theorems. We represent each theorem with a short natural-language description as a retrieval representation and systematically analyze how representation context, language model choice, embedding model, and prompting strategy affect retrieval quality. On a curated evaluation set of theorem-search queries written by professional mathematicians, our approach substantially improves both theorem-level and paper-level retrieval compared to existing baselines, demonstrating that semantic theorem search is feasible and effective at web scale. The theorem search tool is available at https://huggingface.co/spaces/uw-math-ai/theorem-search{this link}, and the dataset is available at https://huggingface.co/datasets/uw-math-ai/TheoremSearch{this link}.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05216</guid>
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<pubDate>Thu, 05 Feb 2026 02:16:20 +0000</pubDate>
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<title>Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR</title>
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<link>https://arxiv.org/abs/2602.05261</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05261.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fanfan Liu, Youyang Yin, Peng Shi, Siqi Yang, Zhixiong Zeng, Haibo Qiu</p><p><b>Upvotes:</b> 45</p><p><b>Summary:</b> Recent applications of Reinforcement Learning with Verifiable Rewards (RLVR) to Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated significant success in enhancing reasoning capabilities for complex tasks. During RLVR training, an increase in response length is often regarded as a key factor contributing to the growth of reasoning ability. However, the patterns of change in response length vary significantly across different RLVR algorithms during the training process. To provide a fundamental explanation for these variations, this paper conducts an in-depth analysis of the components of mainstream RLVR algorithms. We present a theoretical analysis of the factors influencing response length and validate our theory through extensive experimentation. Building upon these theoretical findings, we propose the Length-Unbiased Sequence Policy Optimization (LUSPO) algorithm. Specifically, we rectify the length bias inherent in Group Sequence Policy Optimization (GSPO), rendering its loss function unbiased with respect to response length and thereby resolving the issue of response length collapse. We conduct extensive experiments across mathematical reasoning benchmarks and multimodal reasoning scenarios, where LUSPO consistently achieves superior performance. Empirical results demonstrate that LUSPO represents a novel, state-of-the-art optimization strategy compared to existing methods such as GRPO and GSPO.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05261</guid>
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<pubDate>Thu, 05 Feb 2026 03:35:38 +0000</pubDate>
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<title>ProAct: Agentic Lookahead in Interactive Environments</title>
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<link>https://arxiv.org/abs/2602.05327</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05327.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yangbin Yu, Mingyu Yang, Junyou Li, Yiming Gao, Feiyu Liu, Yijun Yang, Zichuan Lin, Jiafei Lyu, Yicheng Liu, Zhicong Lu, Deheng Ye, Jie Jiang</p><p><b>Upvotes:</b> 22</p><p><b>Summary:</b> Existing Large Language Model (LLM) agents struggle in interactive environments requiring long-horizon planning, primarily due to compounding errors when simulating future states. To address this, we propose ProAct, a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm. First, we introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search. By compressing complex search trees into concise, causal reasoning chains, the agent learns the logic of foresight without the computational overhead of inference-time search. Second, to further refine decision accuracy, we propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms like PPO and GRPO. By leveraging lightweight environment rollouts to calibrate value estimates, MC-Critic provides a low-variance signal that facilitates stable policy optimization without relying on expensive model-based value approximation. Experiments on both stochastic (e.g., 2048) and deterministic (e.g., Sokoban) environments demonstrate that ProAct significantly improves planning accuracy. Notably, a 4B parameter model trained with ProAct outperforms all open-source baselines and rivals state-of-the-art closed-source models, while demonstrating robust generalization to unseen environments. The codes and models are available at https://github.com/GreatX3/ProAct</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05327</guid>
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<pubDate>Thu, 05 Feb 2026 05:45:16 +0000</pubDate>
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<title>Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening</title>
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<link>https://arxiv.org/abs/2602.05386</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05386.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhenxiong Yu, Zhi Yang, Zhiheng Jin, Shuhe Wang, Heng Zhang, Yanlin Fei, Lingfeng Zeng, Fangqi Lou, Shuo Zhang, Tu Hu, Jingping Liu, Rongze Chen, Xingyu Zhu, Kunyi Wang, Chaofa Yuan, Xin Guo, Zhaowei Liu, Feipeng Zhang, Jie Huang, Huacan Wang, Ronghao Chen, Liwen Zhang</p><p><b>Upvotes:</b> 64</p><p><b>Summary:</b> As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agent defense mechanisms adopt a mandatory checking paradigm, in which security validation is forcibly triggered at predefined stages of the agent lifecycle. In this work, we argue that effective agent security should be intrinsic and selective rather than architecturally decoupled and mandatory. We propose Spider-Sense framework, an event-driven defense framework based on Intrinsic Risk Sensing (IRS), which allows agents to maintain latent vigilance and trigger defenses only upon risk perception. Once triggered, the Spider-Sense invokes a hierarchical defence mechanism that trades off efficiency and precision: it resolves known patterns via lightweight similarity matching while escalating ambiguous cases to deep internal reasoning, thereby eliminating reliance on external models. To facilitate rigorous evaluation, we introduce S^2Bench, a lifecycle-aware benchmark featuring realistic tool execution and multi-stage attacks. Extensive experiments demonstrate that Spider-Sense achieves competitive or superior defense performance, attaining the lowest Attack Success Rate (ASR) and False Positive Rate (FPR), with only a marginal latency overhead of 8.3\%.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05386</guid>
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<pubDate>Thu, 05 Feb 2026 07:11:05 +0000</pubDate>
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<title>Reinforcement World Model Learning for LLM-based Agents</title>
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<link>https://arxiv.org/abs/2602.05842</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05842.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiao Yu, Baolin Peng, Ruize Xu, Yelong Shen, Pengcheng He, Suman Nath, Nikhil Singh, Jiangfeng Gao, Zhou Yu</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and adapt to environment dynamics, highlighting the need for world-modeling capabilities in LLM-based agents. We propose Reinforcement World Model Learning (RWML), a self-supervised method that learns action-conditioned world models for LLM-based agents on textual states using sim-to-real gap rewards. Our method aligns simulated next states produced by the model with realized next states observed from the environment, encouraging consistency between internal world simulations and actual environment dynamics in a pre-trained embedding space. Unlike next-state token prediction, which prioritizes token-level fidelity (i.e., reproducing exact wording) over semantic equivalence and can lead to model collapse, our method provides a more robust training signal and is empirically less susceptible to reward hacking than LLM-as-a-judge. We evaluate our method on ALFWorld and τ^2 Bench and observe significant gains over the base model, despite being entirely self-supervised. When combined with task-success rewards, our method outperforms direct task-success reward RL by 6.9 and 5.7 points on ALFWorld and τ^2 Bench respectively, while matching the performance of expert-data training.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05842</guid>
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<pubDate>Thu, 05 Feb 2026 16:30:08 +0000</pubDate>
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<title>Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations</title>
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<link>https://arxiv.org/abs/2602.05885</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05885.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wei Liu, Jiawei Xu, Yingru Li, Longtao Zheng, Tianjian Li, Qian Liu, Junxian He</p><p><b>Upvotes:</b> 22</p><p><b>Summary:</b> High-quality kernel is critical for scalable AI systems, and enabling LLMs to generate such code would advance AI development. However, training LLMs for this task requires sufficient data, a robust environment, and the process is often vulnerable to reward hacking and lazy optimization. In these cases, models may hack training rewards and prioritize trivial correctness over meaningful speedup. In this paper, we systematically study reinforcement learning (RL) for kernel generation. We first design KernelGYM, a robust distributed GPU environment that supports reward hacking check, data collection from multi-turn interactions and long-term RL training. Building on KernelGYM, we investigate effective multi-turn RL methods and identify a biased policy gradient issue caused by self-inclusion in GRPO. To solve this, we propose Turn-level Reinforce-Leave-One-Out (TRLOO) to provide unbiased advantage estimation for multi-turn RL. To alleviate lazy optimization, we incorporate mismatch correction for training stability and introduce Profiling-based Rewards (PR) and Profiling-based Rejection Sampling (PRS) to overcome the issue. The trained model, Dr.Kernel-14B, reaches performance competitive with Claude-4.5-Sonnet in Kernelbench. Finally, we study sequential test-time scaling for Dr.Kernel-14B. On the KernelBench Level-2 subset, 31.6% of the generated kernels achieve at least a 1.2x speedup over the Torch reference, surpassing Claude-4.5-Sonnet (26.7%) and GPT-5 (28.6%). When selecting the best candidate across all turns, this 1.2x speedup rate further increases to 47.8%. All resources, including environment, training code, models, and dataset, are included in https://www.github.com/hkust-nlp/KernelGYM.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05885</guid>
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<pubDate>Thu, 05 Feb 2026 17:01:09 +0000</pubDate>
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<title>RISE-Video: Can Video Generators Decode Implicit World Rules?</title>
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<link>https://arxiv.org/abs/2602.05986</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.05986.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mingxin Liu, Shuran Ma, Shibei Meng, Xiangyu Zhao, Zicheng Zhang, Shaofeng Zhang, Zhihang Zhong, Peixian Chen, Haoyu Cao, Xing Sun, Haodong Duan, Xue Yang</p><p><b>Upvotes:</b> 26</p><p><b>Summary:</b> While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored frontier. To bridge this gap, we present RISE-Video, a pioneering reasoning-oriented benchmark for Text-Image-to-Video (TI2V) synthesis that shifts the evaluative focus from surface-level aesthetics to deep cognitive reasoning. RISE-Video comprises 467 meticulously human-annotated samples spanning eight rigorous categories, providing a structured testbed for probing model intelligence across diverse dimensions, ranging from commonsense and spatial dynamics to specialized subject domains. Our framework introduces a multi-dimensional evaluation protocol consisting of four metrics: Reasoning Alignment, Temporal Consistency, Physical Rationality, and Visual Quality. To further support scalable evaluation, we propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human-centric assessment. Extensive experiments on 11 state-of-the-art TI2V models reveal pervasive deficiencies in simulating complex scenarios under implicit constraints, offering critical insights for the advancement of future world-simulating generative models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.05986</guid>
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<pubDate>Thu, 05 Feb 2026 18:36:10 +0000</pubDate>
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<title>Context Forcing: Consistent Autoregressive Video Generation with Long Context</title>
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<link>https://arxiv.org/abs/2602.06028</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.06028.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuo Chen, Cong Wei, Sun Sun, Ping Nie, Kai Zhou, Ge Zhang, Ming-Hsuan Yang, Wenhu Chen</p><p><b>Upvotes:</b> 30</p><p><b>Summary:</b> Recent approaches to real-time long video generation typically employ streaming tuning strategies, attempting to train a long-context student using a short-context (memoryless) teacher. In these frameworks, the student performs long rollouts but receives supervision from a teacher limited to short 5-second windows. This structural discrepancy creates a critical student-teacher mismatch: the teacher's inability to access long-term history prevents it from guiding the student on global temporal dependencies, effectively capping the student's context length. To resolve this, we propose Context Forcing, a novel framework that trains a long-context student via a long-context teacher. By ensuring the teacher is aware of the full generation history, we eliminate the supervision mismatch, enabling the robust training of models capable of long-term consistency. To make this computationally feasible for extreme durations (e.g., 2 minutes), we introduce a context management system that transforms the linearly growing context into a Slow-Fast Memory architecture, significantly reducing visual redundancy. Extensive results demonstrate that our method enables effective context lengths exceeding 20 seconds -- 2 to 10 times longer than state-of-the-art methods like LongLive and Infinite-RoPE. By leveraging this extended context, Context Forcing preserves superior consistency across long durations, surpassing state-of-the-art baselines on various long video evaluation metrics.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.06028</guid>
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<pubDate>Thu, 05 Feb 2026 18:58:01 +0000</pubDate>
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<title>InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions</title>
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<link>https://arxiv.org/abs/2602.06035</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.06035.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sirui Xu, Samuel Schulter, Morteza Ziyadi, Xialin He, Xiaohan Fei, Yu-Xiong Wang, Liangyan Gui</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Humans rarely plan whole-body interactions with objects at the level of explicit whole-body movements. High-level intentions, such as affordance, define the goal, while coordinated balance, contact, and manipulation can emerge naturally from underlying physical and motor priors. Scaling such priors is key to enabling humanoids to compose and generalize loco-manipulation skills across diverse contexts while maintaining physically coherent whole-body coordination. To this end, we introduce InterPrior, a scalable framework that learns a unified generative controller through large-scale imitation pretraining and post-training by reinforcement learning. InterPrior first distills a full-reference imitation expert into a versatile, goal-conditioned variational policy that reconstructs motion from multimodal observations and high-level intent. While the distilled policy reconstructs training behaviors, it does not generalize reliably due to the vast configuration space of large-scale human-object interactions. To address this, we apply data augmentation with physical perturbations, and then perform reinforcement learning finetuning to improve competence on unseen goals and initializations. Together, these steps consolidate the reconstructed latent skills into a valid manifold, yielding a motion prior that generalizes beyond the training data, e.g., it can incorporate new behaviors such as interactions with unseen objects. We further demonstrate its effectiveness for user-interactive control and its potential for real robot deployment.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.06035</guid>
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<pubDate>Thu, 05 Feb 2026 18:59:27 +0000</pubDate>
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<title>DFlash: Block Diffusion for Flash Speculative Decoding</title>
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<link>https://arxiv.org/abs/2602.06036</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.06036.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jian Chen, Yesheng Liang, Zhijian Liu</p><p><b>Upvotes:</b> 30</p><p><b>Summary:</b> Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency and poor GPU utilization. Speculative decoding mitigates this bottleneck by using a fast draft model whose outputs are verified in parallel by the target LLM; however, existing methods still rely on autoregressive drafting, which remains sequential and limits practical speedups. Diffusion LLMs offer a promising alternative by enabling parallel generation, but current diffusion models typically underperform compared with autoregressive models. In this paper, we introduce DFlash, a speculative decoding framework that employs a lightweight block diffusion model for parallel drafting. By generating draft tokens in a single forward pass and conditioning the draft model on context features extracted from the target model, DFlash enables efficient drafting with high-quality outputs and higher acceptance rates. Experiments show that DFlash achieves over 6x lossless acceleration across a range of models and tasks, delivering up to 2.5x higher speedup than the state-of-the-art speculative decoding method EAGLE-3.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2602.06036</guid>
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<pubDate>Thu, 05 Feb 2026 18:59:30 +0000</pubDate>
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