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<docs>http://www.rssboard.org/rss-specification</docs>
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<lastBuildDate>Thu, 22 Jan 2026 00:03:01 +0000</lastBuildDate>
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<title>Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD</title>
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<title>FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments</title>
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<link>https://arxiv.org/abs/2601.10237</link>
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<link>https://arxiv.org/abs/2601.07853</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.10237.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Murat Bilgehan Ertan, Marten van Dijk</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under worst-case adversarial privacy definitions remain poorly understood. We analyze DP-SGD in the f-differential privacy framework, which characterizes privacy via hypothesis-testing trade-off curves, and study shuffled sampling over a single epoch with M gradient updates. We derive an explicit suboptimal upper bound on the achievable trade-off curve. This result induces a geometric lower bound on the separation κ which is the maximum distance between the mechanism's trade-off curve and the ideal random-guessing line. Because a large separation implies significant adversarial advantage, meaningful privacy requires small κ. However, we prove that enforcing a small separation imposes a strict lower bound on the Gaussian noise multiplier σ, which directly limits the achievable utility. In particular, under the standard worst-case adversarial model, shuffled DP-SGD must satisfy σge 1{2ln M} quadorquad κge 1{8}!left(1-1{4πln M}right), and thus cannot simultaneously achieve strong privacy and high utility. Although this bound vanishes asymptotically as M to infty, the convergence is extremely slow: even for practically relevant numbers of updates the required noise magnitude remains substantial. We further show that the same limitation extends to Poisson subsampling up to constant factors. Our experiments confirm that the noise levels implied by this bound leads to significant accuracy degradation at realistic training settings, thus showing a critical bottleneck in DP-SGD under standard worst-case adversarial assumptions.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.07853.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhi Yang, Runguo Li, Qiqi Qiang, Jiashun Wang, Fangqi Lou, Mengping Li, Dongpo Cheng, Rui Xu, Heng Lian, Shuo Zhang, Xiaolong Liang, Xiaoming Huang, Zheng Wei, Zhaowei Liu, Xin Guo, Huacan Wang, Ronghao Chen, Liwen Zhang</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing actions. To bridge this gap, we propose FinVault, the first execution-grounded security benchmark for financial agents, comprising 31 regulatory case-driven sandbox scenarios with state-writable databases and explicit compliance constraints, together with 107 real-world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false-positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0\% on state-of-the-art models and remaining non-negligible even for the most robust systems (ASR 6.7\%), highlighting the limited transferability of current safety designs and the need for stronger financial-specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.10237</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.07853</guid>
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<pubDate>Thu, 15 Jan 2026 09:50:36 +0000</pubDate>
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<pubDate>Fri, 09 Jan 2026 03:25:45 +0000</pubDate>
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<title>LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals</title>
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<title>FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning</title>
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<link>https://arxiv.org/abs/2601.10700</link>
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<link>https://arxiv.org/abs/2601.11141</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.10700.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Gilat Toker, Nitay Calderon, Ohad Amosy, Roi Reichart</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Recent work evaluates the faithfulness of such explanations by comparing them to reference causal effects estimated from counterfactuals. In practice, existing benchmarks rely on costly human-written counterfactuals that serve as an imperfect proxy. To address this, we introduce a framework for constructing datasets containing structural counterfactual pairs: LIBERTy (LLM-based Interventional Benchmark for Explainability with Reference Targets). LIBERTy is grounded in explicitly defined Structured Causal Models (SCMs) of the text generation, interventions on a concept propagate through the SCM until an LLM generates the counterfactual. We introduce three datasets (disease detection, CV screening, and workplace violence prediction) together with a new evaluation metric, order-faithfulness. Using them, we evaluate a wide range of methods across five models and identify substantial headroom for improving concept-based explanations. LIBERTy also enables systematic analysis of model sensitivity to interventions: we find that proprietary LLMs show markedly reduced sensitivity to demographic concepts, likely due to post-training mitigation. Overall, LIBERTy provides a much-needed benchmark for developing faithful explainability methods.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11141.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tanyu Chen, Tairan Chen, Kai Shen, Zhenghua Bao, Zhihui Zhang, Man Yuan, Yi Shi</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Recent end-to-end spoken dialogue systems leverage speech tokenizers and neural audio codecs to enable LLMs to operate directly on discrete speech representations. However, these models often exhibit limited speaker identity preservation, hindering personalized voice interaction. In this work, we present Chroma 1.0, the first open-source, real-time, end-to-end spoken dialogue model that achieves both low-latency interaction and high-fidelity personalized voice cloning. Chroma achieves sub-second end-to-end latency through an interleaved text-audio token schedule (1:2) that supports streaming generation, while maintaining high-quality personalized voice synthesis across multi-turn conversations. Our experimental results demonstrate that Chroma achieves a 10.96% relative improvement in speaker similarity over the human baseline, with a Real-Time Factor (RTF) of 0.43, while maintaining strong reasoning and dialogue capabilities. Our code and models are publicly available at https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma and https://huggingface.co/FlashLabs/Chroma-4B .</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.10700</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.11141</guid>
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<pubDate>Thu, 15 Jan 2026 18:54:50 +0000</pubDate>
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<pubDate>Fri, 16 Jan 2026 10:00:03 +0000</pubDate>
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<title>Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey</title>
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<title>Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking</title>
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<link>https://arxiv.org/abs/2601.11655</link>
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<link>https://arxiv.org/abs/2601.11387</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11655.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Caihua Li, Lianghong Guo, Yanlin Wang, Daya Guo, Wei Tao, Zhenyu Shan, Mingwei Liu, Jiachi Chen, Haoyu Song, Duyu Tang, Hongyu Zhang, Zibin Zheng</p><p><b>Upvotes:</b> 49</p><p><b>Summary:</b> Issue resolution, a complex Software Engineering (SWE) task integral to real-world development, has emerged as a compelling challenge for artificial intelligence. The establishment of benchmarks like SWE-bench revealed this task as profoundly difficult for large language models, thereby significantly accelerating the evolution of autonomous coding agents. This paper presents a systematic survey of this emerging domain. We begin by examining data construction pipelines, covering automated collection and synthesis approaches. We then provide a comprehensive analysis of methodologies, spanning training-free frameworks with their modular components to training-based techniques, including supervised fine-tuning and reinforcement learning. Subsequently, we discuss critical analyses of data quality and agent behavior, alongside practical applications. Finally, we identify key challenges and outline promising directions for future research. An open-source repository is maintained at https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution to serve as a dynamic resource in this field.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11387.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Greta Warren, Jingyi Sun, Irina Shklovski, Isabelle Augenstein</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-researched. In our study, we systematically varied explanation type, AI prediction certainty, and correctness of AI system advice for non-expert participants, who evaluated the veracity of claims and AI system predictions. Participants were provided the option of easily inspecting the underlying evidence. We found that participants consistently relied on evidence to validate AI claims across all experimental conditions. When participants were presented with natural language explanations, evidence was used less frequently although they relied on it when these explanations seemed insufficient or flawed. Qualitative data suggests that participants attempted to infer evidence source reliability, despite source identities being deliberately omitted. Our results demonstrate that evidence is a key ingredient in how people evaluate the reliability of information presented by an AI system and, in combination with natural language explanations, offers valuable support for decision-making. Further research is urgently needed to understand how evidence ought to be presented and how people engage with it in practice.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.11655</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.11387</guid>
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<pubDate>Thu, 15 Jan 2026 18:55:03 +0000</pubDate>
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<pubDate>Fri, 16 Jan 2026 15:55:31 +0000</pubDate>
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<title>UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation</title>
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<title>sangkuriang: A pseudo-spectral Python library for Korteweg-de Vries soliton simulation</title>
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<link>https://arxiv.org/abs/2601.11522</link>
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<link>https://arxiv.org/abs/2601.12029</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11522.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ruiheng Zhang, Jingfeng Yao, Huangxuan Zhao, Hao Yan, Xiao He, Lei Chen, Zhou Wei, Yong Luo, Zengmao Wang, Lefei Zhang, Dacheng Tao, Bo Du</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstraction versus pixel-level reconstruction. Existing approaches, typically based on parameter-shared autoregressive architectures, frequently lead to compromised performance in one or both tasks. To address this, we present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation. UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation. Crucially, a cross-modal self-attention mechanism is introduced to dynamically guide the generation process with understanding features. Coupled with a rigorous data cleaning pipeline and a multi-stage training strategy, this architecture enables synergistic collaboration between tasks while leveraging the strengths of diffusion models for superior generation. On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance (Micro-F1) and a 24.2% gain in generation quality (FD-RadDino), using only a quarter of the parameters of LLM-CXR. By achieving performance on par with task-specific models, our work establishes a scalable paradigm for synergistic medical image understanding and generation. Codes and models are available at https://github.com/ZrH42/UniX.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12029.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sandy H. S. Herho, Faruq Khadami, Iwan P. Anwar, Dasapta E. Irawan</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> The Korteweg-de Vries (KdV) equation serves as a foundational model in nonlinear wave physics, describing the balance between dispersive spreading and nonlinear steepening that gives rise to solitons. This article introduces sangkuriang, an open-source Python library for solving this equation using Fourier pseudo-spectral spatial discretization coupled with adaptive high-order time integration. The implementation leverages just-in-time (JIT) compilation for computational efficiency while maintaining accessibility for instructional purposes. Validation encompasses progressively complex scenarios including isolated soliton propagation, symmetric two-wave configurations, overtaking collisions between waves of differing amplitudes, and three-body interactions. Conservation of the classical invariants is monitored throughout, with deviations remaining small across all test cases. Measured soliton velocities conform closely to theoretical predictions based on the amplitude-velocity relationship characteristic of integrable systems. Complementary diagnostics drawn from information theory and recurrence analysis confirm that computed solutions preserve the regular phase-space structure expected for completely integrable dynamics. The solver outputs data in standard scientific formats compatible with common analysis tools and generates visualizations of spatiotemporal wave evolution. By combining numerical accuracy with practical accessibility on modest computational resources, sangkuriang offers a platform suitable for both classroom demonstrations of nonlinear wave phenomena and exploratory research into soliton dynamics.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.11522</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12029</guid>
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<pubDate>Fri, 16 Jan 2026 18:59:58 +0000</pubDate>
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<pubDate>Sat, 17 Jan 2026 12:12:52 +0000</pubDate>
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<title>Agentic-R: Learning to Retrieve for Agentic Search</title>
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<title>MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents</title>
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<link>https://arxiv.org/abs/2601.11888</link>
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<link>https://arxiv.org/abs/2601.12346</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11888.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wenhan Liu, Xinyu Ma, Yutao Zhu, Yuchen Li, Daiting Shi, Dawei Yin, Zhicheng Dou</p><p><b>Upvotes:</b> 10</p><p><b>Summary:</b> Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored. Existing search agents typically rely on similarity-based retrievers, while similar passages are not always useful for final answer generation. In this paper, we propose a novel retriever training framework tailored for agentic search. Unlike retrievers designed for single-turn retrieval-augmented generation (RAG) that only rely on local passage utility, we propose to use both local query-passage relevance and global answer correctness to measure passage utility in a multi-turn agentic search. We further introduce an iterative training strategy, where the search agent and the retriever are optimized bidirectionally and iteratively. Different from RAG retrievers that are only trained once with fixed questions, our retriever is continuously improved using evolving and higher-quality queries from the agent. Extensive experiments on seven single-hop and multi-hop QA benchmarks demonstrate that our retriever, termed , consistently outperforms strong baselines across different search agents. Our codes are available at: https://github.com/8421BCD/Agentic-R.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12346.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Peizhou Huang, Zixuan Zhong, Zhongwei Wan, Donghao Zhou, Samiul Alam, Xin Wang, Zexin Li, Zhihao Dou, Li Zhu, Jing Xiong, Chaofan Tao, Yan Xu, Dimitrios Dimitriadis, Tuo Zhang, Mi Zhang</p><p><b>Upvotes:</b> 41</p><p><b>Summary:</b> Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains, where each task provides an image-text bundle to evaluate multimodal understanding and citation-grounded report generation. Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use, where models must connect visual artifacts to sourced claims and maintain consistency across narrative, citations, and visual references. We further propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity, each producing fine-grained signals that support error diagnosis beyond a single overall score. Experiments across 25 state-of-the-art models reveal systematic trade-offs between generation quality, citation discipline, and multimodal grounding, highlighting that strong prose alone does not guarantee faithful evidence use and that multimodal integrity remains a key bottleneck for deep research agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.11888</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12346</guid>
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<pubDate>Sat, 17 Jan 2026 02:59:54 +0000</pubDate>
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<pubDate>Sun, 18 Jan 2026 10:41:33 +0000</pubDate>
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<title>RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection</title>
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<title>Agentic Reasoning for Large Language Models</title>
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<link>https://arxiv.org/abs/2601.11898</link>
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<link>https://arxiv.org/abs/2601.12538</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11898.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yilmaz Korkmaz, Vishal M. Patel</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Remote sensing change detection aims to localize and characterize scene changes between two time points and is central to applications such as environmental monitoring and disaster assessment. Meanwhile, visual autoregressive models (VARs) have recently shown impressive image generation capability, but their adoption for pixel-level discriminative tasks remains limited due to weak controllability, suboptimal dense prediction performance and exposure bias. We introduce RemoteVAR, a new VAR-based change detection framework that addresses these limitations by conditioning autoregressive prediction on multi-resolution fused bi-temporal features via cross-attention, and by employing an autoregressive training strategy designed specifically for change map prediction. Extensive experiments on standard change detection benchmarks show that RemoteVAR delivers consistent and significant improvements over strong diffusion-based and transformer-based baselines, establishing a competitive autoregressive alternative for remote sensing change detection. Code will be available https://github.com/yilmazkorkmaz1/RemoteVAR{here}.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12538.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tianxin Wei, Ting-Wei Li, Zhining Liu, Xuying Ning, Ze Yang, Jiaru Zou, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Dongqi Fu, Zihao Li, Mengting Ai, Duo Zhou, Wenxuan Bao, Yunzhe Li, Gaotang Li, Cheng Qian, Yu Wang, Xiangru Tang, Yin Xiao, Liri Fang, Hui Liu, Xianfeng Tang, Yuji Zhang, Chi Wang, Jiaxuan You, Heng Ji, Hanghang Tong, Jingrui He</p><p><b>Upvotes:</b> 125</p><p><b>Summary:</b> Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.11898</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12538</guid>
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<pubDate>Sat, 17 Jan 2026 03:50:00 +0000</pubDate>
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<pubDate>Sun, 18 Jan 2026 18:58:23 +0000</pubDate>
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<title>MemoryRewardBench: Benchmarking Reward Models for Long-Term Memory Management in Large Language Models</title>
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<title>Typhoon ASR Real-time: FastConformer-Transducer for Thai Automatic Speech Recognition</title>
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<link>https://arxiv.org/abs/2601.11969</link>
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<link>https://arxiv.org/abs/2601.13044</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.11969.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zecheng Tang, Baibei Ji, Ruoxi Sun, Haitian Wang, WangJie You, Zhang Yijun, Wenpeng Zhu, Ji Qi, Juntao Li, Min Zhang</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Existing works increasingly adopt memory-centric mechanisms to process long contexts in a segment manner, and effective memory management is one of the key capabilities that enables large language models to effectively propagate information across the entire sequence. Therefore, leveraging reward models (RMs) to automatically and reliably evaluate memory quality is critical. In this work, we introduce MemoryRewardBench, the first benchmark to systematically study the ability of RMs to evaluate long-term memory management processes. MemoryRewardBench covers both long-context comprehension and long-form generation tasks, featuring 10 distinct settings with different memory management patterns, with context length ranging from 8K to 128K tokens. Evaluations on 13 cutting-edge RMs indicate a diminishing performance gap between open-source and proprietary models, with newer-generation models consistently outperforming their predecessors regardless of parameter count. We further expose the capabilities and fundamental limitations of current RMs in evaluating LLM memory management across diverse settings.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13044.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Warit Sirichotedumrong, Adisai Na-Thalang, Potsawee Manakul, Pittawat Taveekitworachai, Sittipong Sripaisarnmongkol, Kunat Pipatanakul</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> Large encoder-decoder models like Whisper achieve strong offline transcription but remain impractical for streaming applications due to high latency. However, due to the accessibility of pre-trained checkpoints, the open Thai ASR landscape remains dominated by these offline architectures, leaving a critical gap in efficient streaming solutions. We present Typhoon ASR Real-time, a 115M-parameter FastConformer-Transducer model for low-latency Thai speech recognition. We demonstrate that rigorous text normalization can match the impact of model scaling: our compact model achieves a 45x reduction in computational cost compared to Whisper Large-v3 while delivering comparable accuracy. Our normalization pipeline resolves systemic ambiguities in Thai transcription --including context-dependent number verbalization and repetition markers (mai yamok) --creating consistent training targets. We further introduce a two-stage curriculum learning approach for Isan (north-eastern) dialect adaptation that preserves Central Thai performance. To address reproducibility challenges in Thai ASR, we release the Typhoon ASR Benchmark, a gold-standard human-labeled datasets with transcriptions following established Thai linguistic conventions, providing standardized evaluation protocols for the research community.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13044</guid>
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<pubDate>Sat, 17 Jan 2026 09:04:53 +0000</pubDate>
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<pubDate>Mon, 19 Jan 2026 13:28:17 +0000</pubDate>
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<title>ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents</title>
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<title>CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning</title>
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<link>https://arxiv.org/abs/2601.12294</link>
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<link>https://arxiv.org/abs/2601.13262</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12294.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dawei Li, Yuguang Yao, Zhen Tan, Huan Liu, Ruocheng Guo</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rewards, enabling more fine-grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool-using settings. In this paper, we introduce ToolPRMBench, a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents. ToolPRMBench is built on top of several representative tool-using benchmarks and converts agent trajectories into step-level test cases. Each case contains the interaction history, a correct action, a plausible but incorrect alternative, and relevant tool metadata. We respectively utilize offline sampling to isolate local single-step errors and online sampling to capture realistic multi-step failures from full agent rollouts. A multi-LLM verification pipeline is proposed to reduce label noise and ensure data quality. We conduct extensive experiments across large language models, general PRMs, and tool-specialized PRMs on ToolPRMBench. The results reveal clear differences in PRM effectiveness and highlight the potential of specialized PRMs for tool-using. Code and data will be released at https://github.com/David-Li0406/ToolPRMBench.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13262.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Eric Onyame, Akash Ghosh, Subhadip Baidya, Sriparna Saha, Xiuying Chen, Chirag Agarwal</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by first introducing CUREMED-BENCH, a high-quality multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer, spanning thirteen languages, including underrepresented languages such as Amharic, Yoruba, and Swahili. Building on this dataset, we propose CURE-MED, a curriculum-informed reinforcement learning framework that integrates code-switching-aware supervised fine-tuning and Group Relative Policy Optimization to jointly improve logical correctness and language stability. Across thirteen languages, our approach consistently outperforms strong baselines and scales effectively, achieving 85.21% language consistency and 54.35% logical correctness at 7B parameters, and 94.96% language consistency and 70.04% logical correctness at 32B parameters. These results support reliable and equitable multilingual medical reasoning in LLMs. The code and dataset are available at https://cure-med.github.io/</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12294</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13262</guid>
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<pubDate>Sun, 18 Jan 2026 07:48:36 +0000</pubDate>
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<pubDate>Mon, 19 Jan 2026 17:51:00 +0000</pubDate>
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<title>SciCoQA: Quality Assurance for Scientific Paper--Code Alignment</title>
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<title>Behavior Knowledge Merge in Reinforced Agentic Models</title>
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<link>https://arxiv.org/abs/2601.12910</link>
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<link>https://arxiv.org/abs/2601.13572</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12910.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tim Baumgärtner, Iryna Gurevych</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations. We construct SciCoQA from GitHub issues and reproducibility papers, and to scale our dataset, we propose a synthetic data generation method for constructing paper-code discrepancies. We analyze the paper-code discrepancies in detail and propose discrepancy types and categories to better understand the occurring mismatches. In total, our dataset consists of 611 paper-code discrepancies (81 real, 530 synthetic), spanning diverse computational science disciplines, including AI, Physics, Quantitative Biology, and others. Our evaluation of 21 LLMs highlights the difficulty of SciCoQA, particularly for instances involving omitted paper details, long-context inputs, and data outside the models' pre-training corpus. The best performing model in our evaluation, GPT-5, can only detect 45.7\% of real-world paper-code discrepancies.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13572.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiangchi Yuan, Dachuan Shi, Chunhui Zhang, Zheyuan Liu, Shenglong Yao, Soroush Vosoughi, Wenke Lee</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, existing merging methods are designed for supervised fine-tuning (SFT), and they are suboptimal to preserve task-specific capabilities on RL-trained agentic models. The root is a task-vector mismatch between RL and SFT: on-policy RL induces task vectors that are highly sparse and heterogeneous, whereas SFT-style merging implicitly assumes dense and globally comparable task vectors. When standard global averaging is applied under this mismatch, RL's non-overlapping task vectors that encode critical task-specific behaviors are reduced and parameter updates are diluted. To address this issue, we propose Reinforced Agent Merging (RAM), a distribution-aware merging framework explicitly designed for RL-trained agentic models. RAM disentangles shared and task-specific unique parameter updates, averaging shared components while selectively preserving and rescaling unique ones to counteract parameter update dilution. Experiments across multiple agent domains and model architectures demonstrate that RAM not only surpasses merging baselines, but also unlocks synergistic potential among agents to achieve performance superior to that of specialized agents in their domains.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12910</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13572</guid>
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<pubDate>Mon, 19 Jan 2026 10:04:33 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 03:56:53 +0000</pubDate>
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<title>On the Evidentiary Limits of Membership Inference for Copyright Auditing</title>
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<title>AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization</title>
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<link>https://arxiv.org/abs/2601.12937</link>
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<link>https://arxiv.org/abs/2601.13918</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12937.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Murat Bilgehan Ertan, Emirhan Böge, Min Chen, Kaleel Mahmood, Marten van Dijk</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> As large language models (LLMs) are trained on increasingly opaque corpora, membership inference attacks (MIAs) have been proposed to audit whether copyrighted texts were used during training, despite growing concerns about their reliability under realistic conditions. We ask whether MIAs can serve as admissible evidence in adversarial copyright disputes where an accused model developer may obfuscate training data while preserving semantic content, and formalize this setting through a judge-prosecutor-accused communication protocol. To test robustness under this protocol, we introduce SAGE (Structure-Aware SAE-Guided Extraction), a paraphrasing framework guided by Sparse Autoencoders (SAEs) that rewrites training data to alter lexical structure while preserving semantic content and downstream utility. Our experiments show that state-of-the-art MIAs degrade when models are fine-tuned on SAGE-generated paraphrases, indicating that their signals are not robust to semantics-preserving transformations. While some leakage remains in certain fine-tuning regimes, these results suggest that MIAs are brittle in adversarial settings and insufficient, on their own, as a standalone mechanism for copyright auditing of LLMs.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13918.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yusheng Liao, Chuan Xuan, Yutong Cai, Lina Yang, Zhe Chen, Yanfeng Wang, Yu Wang</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12937</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13918</guid>
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<pubDate>Mon, 19 Jan 2026 10:46:51 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 12:48:04 +0000</pubDate>
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<title>Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization</title>
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<title>Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematics</title>
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<link>https://arxiv.org/abs/2601.12993</link>
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<link>https://arxiv.org/abs/2601.14027</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.12993.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hao Luo, Ye Wang, Wanpeng Zhang, Sipeng Zheng, Ziheng Xi, Chaoyi Xu, Haiweng Xu, Haoqi Yuan, Chi Zhang, Yiqing Wang, Yicheng Feng, Zongqing Lu</p><p><b>Upvotes:</b> 59</p><p><b>Summary:</b> We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose a human-centric learning paradigm that treats human interaction traces as a universal "mother tongue" for physical interaction. To support this, we present UniHand-2.0, the largest embodied pre-training recipe to date, comprising over 35,000 hours of multimodal data across 30 distinct robotic embodiments. Our approach introduces a Unified Action Space that maps heterogeneous robot controls into semantically aligned slots, enabling low-resource robots to bootstrap skills from human data and high-resource platforms. Built upon this human-centric foundation, we design a unified sequential modeling and multi-task pre-training paradigm to bridge human demonstrations and robotic execution. Architecturally, Being-H0.5 utilizes a Mixture-of-Transformers design featuring a novel Mixture-of-Flow (MoF) framework to decouple shared motor primitives from specialized embodiment-specific experts. Finally, to make cross-embodiment policies stable in the real world, we introduce Manifold-Preserving Gating for robustness under sensory shift and Universal Async Chunking to universalize chunked control across embodiments with different latency and control profiles. We empirically demonstrate that Being-H0.5 achieves state-of-the-art results on simulated benchmarks, such as LIBERO (98.9%) and RoboCasa (53.9%), while also exhibiting strong cross-embodiment capabilities on five robotic platforms.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14027.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Junqi Liu, Zihao Zhou, Zekai Zhu, Marco Dos Santos, Weikun He, Jiawei Liu, Ran Wang, Yunzhou Xie, Junqiao Zhao, Qiufeng Wang, Lihong Zhi, Jia Li, Wenda Li</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Agentic systems have recently become the dominant paradigm for formal theorem proving, achieving strong performance by coordinating multiple models and tools. However, existing approaches often rely on task-specific pipelines and trained formal provers, limiting their flexibility and reproducibility. In this paper, we propose the paradigm that directly uses a general coding agent as a formal math reasoner. This paradigm is motivated by (1) A general coding agent provides a natural interface for diverse reasoning tasks beyond proving, (2) Performance can be improved by simply replacing the underlying base model, without training, and (3) MCP enables flexible extension and autonomous calling of specialized tools, avoiding complex design. Based on this paradigm, we introduce Numina-Lean-Agent, which combines Claude Code with Numina-Lean-MCP to enable autonomous interaction with Lean, retrieval of relevant theorems, informal proving and auxiliary reasoning tools. Using Claude Opus 4.5 as the base model, Numina-Lean-Agent solves all problems in Putnam 2025 (12 / 12), matching the best closed-source system. Beyond benchmark evaluation, we further demonstrate its generality by interacting with mathematicians to successfully formalize the Brascamp-Lieb theorem. We release Numina-Lean-Agent and all solutions at https://github.com/project-numina/numina-lean-agent.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.12993</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14027</guid>
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<pubDate>Mon, 19 Jan 2026 12:20:38 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 14:51:45 +0000</pubDate>
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<title>Think3D: Thinking with Space for Spatial Reasoning</title>
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<title>Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models</title>
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||||||
<link>https://arxiv.org/abs/2601.13029</link>
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<link>https://arxiv.org/abs/2601.14152</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13029.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zaibin Zhang, Yuhan Wu, Lianjie Jia, Yifan Wang, Zhongbo Zhang, Yijiang Li, Binghao Ran, Fuxi Zhang, Zhuohan Sun, Zhenfei Yin, Lijun Wang, Huchuan Lu</p><p><b>Upvotes:</b> 28</p><p><b>Summary:</b> Understanding and reasoning about the physical world requires spatial intelligence: the ability to interpret geometry, perspective, and spatial relations beyond 2D perception. While recent vision large models (VLMs) excel at visual understanding, they remain fundamentally 2D perceivers and struggle with genuine 3D reasoning. We introduce Think3D, a framework that enables VLM agents to think with 3D space. By leveraging 3D reconstruction models that recover point clouds and camera poses from images or videos, Think3D allows the agent to actively manipulate space through camera-based operations and ego/global-view switching, transforming spatial reasoning into an interactive 3D chain-of-thought process. Without additional training, Think3D significantly improves the spatial reasoning performance of advanced models such as GPT-4.1 and Gemini 2.5 Pro, yielding average gains of +7.8% on BLINK Multi-view and MindCube, and +4.7% on VSI-Bench. We further show that smaller models, which struggle with spatial exploration, benefit significantly from a reinforcement learning policy that enables the model to select informative viewpoints and operations. With RL, the benefit from tool usage increases from +0.7% to +6.8%. Our findings demonstrate that training-free, tool-augmented spatial exploration is a viable path toward more flexible and human-like 3D reasoning in multimodal agents, establishing a new dimension of multimodal intelligence. Code and weights are released at https://github.com/zhangzaibin/spagent.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14152.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hyunjong Ok, Jaeho Lee</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Large language models exhibit surprising sensitivity to the structure of the prompt, but the mechanisms underlying this sensitivity remain poorly understood. In this work, we conduct an in-depth investigation on a striking case: in multiple-choice question answering, placing context before the questions and options (CQO) outperforms the reverse order (QOC) by over 14%p, consistently over a wide range of models and datasets. Through systematic architectural analysis, we identify causal attention as the core mechanism: in QOC prompts, the causal mask prevents option tokens from attending to context, creating an information bottleneck where context becomes invisible to options.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13029</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14152</guid>
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<pubDate>Mon, 19 Jan 2026 13:13:54 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 16:54:22 +0000</pubDate>
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<title>METIS: Mentoring Engine for Thoughtful Inquiry & Solutions</title>
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<title>RoboBrain 2.5: Depth in Sight, Time in Mind</title>
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<link>https://arxiv.org/abs/2601.13075</link>
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<link>https://arxiv.org/abs/2601.14352</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13075.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Abhinav Rajeev Kumar, Dhruv Trehan, Paras Chopra</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Many students lack access to expert research mentorship. We ask whether an AI mentor can move undergraduates from an idea to a paper. We build METIS, a tool-augmented, stage-aware assistant with literature search, curated guidelines, methodology checks, and memory. We evaluate METIS against GPT-5 and Claude Sonnet 4.5 across six writing stages using LLM-as-a-judge pairwise preferences, student-persona rubrics, short multi-turn tutoring, and evidence/compliance checks. On 90 single-turn prompts, LLM judges preferred METIS to Claude Sonnet 4.5 in 71% and to GPT-5 in 54%. Student scores (clarity/actionability/constraint-fit; 90 prompts x 3 judges) are higher across stages. In multi-turn sessions (five scenarios/agent), METIS yields slightly higher final quality than GPT-5. Gains concentrate in document-grounded stages (D-F), consistent with stage-aware routing and groundings failure modes include premature tool routing, shallow grounding, and occasional stage misclassification.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14352.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Huajie Tan, Enshen Zhou, Zhiyu Li, Yijie Xu, Yuheng Ji, Xiansheng Chen, Cheng Chi, Pengwei Wang, Huizhu Jia, Yulong Ao, Mingyu Cao, Sixiang Chen, Zhe Li, Mengzhen Liu, Zixiao Wang, Shanyu Rong, Yaoxu Lyu, Zhongxia Zhao, Peterson Co, Yibo Li, Yi Han, Shaoxuan Xie, Guocai Yao, Songjing Wang, Leiduo Zhang, Xi Yang, Yance Jiao, Donghai Shi, Kunchang Xie, Shaokai Nie, Chunlei Men, Yonghua Lin, Zhongyuan Wang, Tiejun Huang, Shanghang Zhang</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> We introduce RoboBrain 2.5, a next-generation embodied AI foundation model that advances general perception, spatial reasoning, and temporal modeling through extensive training on high-quality spatiotemporal supervision. Building upon its predecessor, RoboBrain 2.5 introduces two major capability upgrades. Specifically, it unlocks Precise 3D Spatial Reasoning by shifting from 2D pixel-relative grounding to depth-aware coordinate prediction and absolute metric constraint comprehension, generating complete 3D manipulation traces as ordered keypoint sequences under physical constraints. Complementing this spatial precision, the model establishes Dense Temporal Value Estimation that provides dense, step-aware progress prediction and execution state understanding across varying viewpoints, producing stable feedback signals for downstream learning. Together, these upgrades extend the framework toward more physically grounded and execution-aware embodied intelligence for complex, fine-grained manipulation. The code and checkpoints are available at project website: https://superrobobrain.github.io</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13075</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14352</guid>
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<pubDate>Mon, 19 Jan 2026 14:10:35 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 17:21:54 +0000</pubDate>
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<title>Aligning Agentic World Models via Knowledgeable Experience Learning</title>
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<title>Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance</title>
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<link>https://arxiv.org/abs/2601.13247</link>
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<link>https://arxiv.org/abs/2601.14171</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13247.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations-generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation is inherently rigid, struggling to adapt to the open-ended variability of physical dynamics without continuous, costly retraining. To bridge this gap, we introduce WorldMind, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback. Specifically, it unifies Process Experience to enforce physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories. Experiments on EB-ALFRED and EB-Habitat demonstrate that WorldMind achieves superior performance compared to baselines with remarkable cross-model and cross-environment transferability.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14171.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, Zhipeng Zhang</p><p><b>Upvotes:</b> 35</p><p><b>Summary:</b> Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce RebuttalAgent, the first multi-agents framework that reframes rebuttal generation as an evidence-centric planning task. Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts by synthesizing compressed summaries with high-fidelity text while integrating an autonomous and on-demand external search module to resolve concerns requiring outside literature. By generating an inspectable response plan before drafting, RebuttalAgent ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed RebuttalBench and demonstrate that our pipeline outperforms strong baselines in coverage, faithfulness, and strategic coherence, offering a transparent and controllable assistant for the peer review process. Code will be released.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13247</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14171</guid>
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<pubDate>Mon, 19 Jan 2026 17:33:31 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 17:23:51 +0000</pubDate>
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<title>Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph</title>
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<title>XR: Cross-Modal Agents for Composed Image Retrieval</title>
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||||||
<link>https://arxiv.org/abs/2601.13251</link>
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<link>https://arxiv.org/abs/2601.14245</link>
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||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13251.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ebubekir Tosun, Mehmet Emin Buldur, Özay Ezerceli, Mahmoud ElHussieni</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Neural embeddings have a notorious blind spot: they can't reliably tell synonyms apart from antonyms. Consequently, increasing similarity thresholds often fails to prevent opposites from being grouped together. We've built a large-scale semantic clustering system specifically designed to tackle this problem head on. Our pipeline chews through 15 million lexical items, evaluates a massive 520 million potential relationships, and ultimately generates 2.9 million high-precision semantic clusters. The system makes three primary contributions. First, we introduce a labeled dataset of 843,000 concept pairs spanning synonymy, antonymy, and co-hyponymy, constructed via Gemini 2.5-Flash LLM augmentation and verified using human-curated dictionary resources. Second, we propose a specialized three-way semantic relation discriminator that achieves 90% macro-F1, enabling robust disambiguation beyond raw embedding similarity. Third, we introduce a novel soft-to-hard clustering algorithm that mitigates semantic drift preventing erroneous transitive chains (e.g., hot -> spicy -> pain -> depression) while simultaneously resolving polysemy. Our approach employs a topology-aware two-stage expansion-pruning procedure with topological voting, ensuring that each term is assigned to exactly one semantically coherent cluster. The resulting resource enables high-precision semantic search and retrieval-augmented generation, particularly for morphologically rich and low-resource languages where existing synonym databases remain sparse.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14245.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhongyu Yang, Wei Pang, Yingfang Yuan</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as each query combines a reference image with textual modifications, requiring compositional understanding across modalities. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. It orchestrates three specialized types of agents: imagination agents synthesize target representations through cross-modal generation, similarity agents perform coarse filtering via hybrid matching, and question agents verify factual consistency through targeted reasoning for fine filtering. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. Code is available: https://01yzzyu.github.io/xr.github.io/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13251</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14245</guid>
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<pubDate>Mon, 19 Jan 2026 17:37:25 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 18:57:00 +0000</pubDate>
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<title>A Hybrid Protocol for Large-Scale Semantic Dataset Generation in Low-Resource Languages: The Turkish Semantic Relations Corpus</title>
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<title>Motion 3-to-4: 3D Motion Reconstruction for 4D Synthesis</title>
|
||||||
<link>https://arxiv.org/abs/2601.13253</link>
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<link>https://arxiv.org/abs/2601.14253</link>
|
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13253.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ebubekir Tosun, Mehmet Emin Buldur, Özay Ezerceli, Mahmoud ElHussieni</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> We present a hybrid methodology for generating large-scale semantic relationship datasets in low-resource languages, demonstrated through a comprehensive Turkish semantic relations corpus. Our approach integrates three phases: (1) FastText embeddings with Agglomerative Clustering to identify semantic clusters, (2) Gemini 2.5-Flash for automated semantic relationship classification, and (3) integration with curated dictionary sources. The resulting dataset comprises 843,000 unique Turkish semantic pairs across three relationship types (synonyms, antonyms, co-hyponyms) representing a 10x scale increase over existing resources at minimal cost ($65). We validate the dataset through two downstream tasks: an embedding model achieving 90% top-1 retrieval accuracy and a classification model attaining 90% F1-macro. Our scalable protocol addresses critical data scarcity in Turkish NLP and demonstrates applicability to other low-resource languages. We publicly release the dataset and models.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14253.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hongyuan Chen, Xingyu Chen, Youjia Zhang, Zexiang Xu, Anpei Chen</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> We present Motion 3-to-4, a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video and an optional 3D reference mesh. While recent advances have significantly improved 2D, video, and 3D content generation, 4D synthesis remains difficult due to limited training data and the inherent ambiguity of recovering geometry and motion from a monocular viewpoint. Motion 3-to-4 addresses these challenges by decomposing 4D synthesis into static 3D shape generation and motion reconstruction. Using a canonical reference mesh, our model learns a compact motion latent representation and predicts per-frame vertex trajectories to recover complete, temporally coherent geometry. A scalable frame-wise transformer further enables robustness to varying sequence lengths. Evaluations on both standard benchmarks and a new dataset with accurate ground-truth geometry show that Motion 3-to-4 delivers superior fidelity and spatial consistency compared to prior work. Project page is available at https://motion3-to-4.github.io/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13253</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14253</guid>
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<pubDate>Mon, 19 Jan 2026 17:38:52 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 18:59:48 +0000</pubDate>
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<title>A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification</title>
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<title>Implicit Neural Representation Facilitates Unified Universal Vision Encoding</title>
|
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<link>https://arxiv.org/abs/2601.13288</link>
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<link>https://arxiv.org/abs/2601.14256</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13288.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Gonzalo Ariel Meyoyan, Luciano Del Corro</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operational complexity. We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation. We frame classification as representation selection over the full token-layer hidden-state tensor, rather than committing to a fixed token or fixed layer (e.g., first-token logits or final-layer pooling). To implement this, we introduce a two-stage aggregator that (i) summarizes tokens within each layer and (ii) aggregates across layer summaries to form a single representation for classification. We instantiate this template with direct pooling, a 100K-parameter scoring-attention gate, and a downcast multi-head self-attention (MHA) probe with up to 35M trainable parameters. Across safety and sentiment benchmarks our probes improve over logit-only reuse (e.g., MULI) and are competitive with substantially larger task-specific baselines, while preserving near-serving latency and avoiding the VRAM and latency costs of a separate guard-model pipeline.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14256.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Matthew Gwilliam, Xiao Wang, Xuefeng Hu, Zhenheng Yang</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Models for image representation learning are typically designed for either recognition or generation. Various forms of contrastive learning help models learn to convert images to embeddings that are useful for classification, detection, and segmentation. On the other hand, models can be trained to reconstruct images with pixel-wise, perceptual, and adversarial losses in order to learn a latent space that is useful for image generation. We seek to unify these two directions with a first-of-its-kind model that learns representations which are simultaneously useful for recognition and generation. We train our model as a hyper-network for implicit neural representation, which learns to map images to model weights for fast, accurate reconstruction. We further integrate our INR hyper-network with knowledge distillation to improve its generalization and performance. Beyond the novel training design, the model also learns an unprecedented compressed embedding space with outstanding performance for various visual tasks. The complete model competes with state-of-the-art results for image representation learning, while also enabling generative capabilities with its high-quality tiny embeddings. The code is available at https://github.com/tiktok/huvr.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13288</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14256</guid>
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<pubDate>Mon, 19 Jan 2026 18:40:29 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 18:59:57 +0000</pubDate>
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<title>DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems</title>
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<title>Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis</title>
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<link>https://arxiv.org/abs/2601.13591</link>
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<link>https://arxiv.org/abs/2601.14417</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13591.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Maojun Sun, Yifei Xie, Yue Wu, Ruijian Han, Binyan Jiang, Defeng Sun, Yancheng Yuan, Jian Huang</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, which often span multiple taxonomies and lack standard answers, poses a significant challenge for evaluation. To address this, we introduce DSAEval, a benchmark comprising 641 real-world data science problems grounded in 285 diverse datasets, covering both structured and unstructured data (e.g., vision and text). DSAEval incorporates three distinctive features: (1) Multimodal Environment Perception, which enables agents to interpret observations from multiple modalities including text and vision; (2) Multi-Query Interactions, which mirror the iterative and cumulative nature of real-world data science projects; and (3) Multi-Dimensional Evaluation, which provides a holistic assessment across reasoning, code, and results. We systematically evaluate 11 advanced agentic LLMs using DSAEval. Our results show that Claude-Sonnet-4.5 achieves the strongest overall performance, GPT-5.2 is the most efficient, and MiMo-V2-Flash is the most cost-effective. We further demonstrate that multimodal perception consistently improves performance on vision-related tasks, with gains ranging from 2.04% to 11.30%. Overall, while current data science agents perform well on structured data and routine data anlysis workflows, substantial challenges remain in unstructured domains. Finally, we offer critical insights and outline future research directions to advance the development of data science agents.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14417.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Thanathai Lertpetchpun, Yoonjeong Lee, Thanapat Trachu, Jihwan Lee, Tiantian Feng, Dani Byrd, Shrikanth Narayanan</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Many spoken languages, including English, exhibit wide variation in dialects and accents, making accent control an important capability for flexible text-to-speech (TTS) models. Current TTS systems typically generate accented speech by conditioning on speaker embeddings associated with specific accents. While effective, this approach offers limited interpretability and controllability, as embeddings also encode traits such as timbre and emotion. In this study, we analyze the interaction between speaker embeddings and linguistically motivated phonological rules in accented speech synthesis. Using American and British English as a case study, we implement rules for flapping, rhoticity, and vowel correspondences. We propose the phoneme shift rate (PSR), a novel metric quantifying how strongly embeddings preserve or override rule-based transformations. Experiments show that combining rules with embeddings yields more authentic accents, while embeddings can attenuate or overwrite rules, revealing entanglement between accent and speaker identity. Our findings highlight rules as a lever for accent control and a framework for evaluating disentanglement in speech generation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13591</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14417</guid>
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<pubDate>Tue, 20 Jan 2026 04:44:36 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 19:25:33 +0000</pubDate>
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<title>Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging</title>
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<title>GutenOCR: A Grounded Vision-Language Front-End for Documents</title>
|
||||||
<link>https://arxiv.org/abs/2601.13677</link>
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<link>https://arxiv.org/abs/2601.14490</link>
|
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13677.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl, Till J. Bungert, Lukas Klein, Lars Krämer, Paul F. Jäger, Klaus Maier-Hein, Fabian Isensee</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random sampling baselines adapted to 3D data, leaving the field without a reliable solution. We introduce Class-stratified Scheduled Power Predictive Entropy (ClaSP PE), a simple and effective query strategy that addresses two key limitations of standard uncertainty-based AL methods: class imbalance and redundancy in early selections. ClaSP PE combines class-stratified querying to ensure coverage of underrepresented structures and log-scale power noising with a decaying schedule to enforce query diversity in early-stage AL and encourage exploitation later. In our evaluation on 24 experimental settings using four 3D biomedical datasets within the comprehensive nnActive benchmark, ClaSP PE is the only method that generally outperforms improved random baselines in terms of both segmentation quality with statistically significant gains, whilst remaining annotation efficient. Furthermore, we explicitly simulate the real-world application by testing our method on four previously unseen datasets without manual adaptation, where all experiment parameters are set according to predefined guidelines. The results confirm that ClaSP PE robustly generalizes to novel tasks without requiring dataset-specific tuning. Within the nnActive framework, we present compelling evidence that an AL method can consistently outperform random baselines adapted to 3D segmentation, in terms of both performance and annotation efficiency in a realistic, close-to-production scenario. Our open-source implementation and clear deployment guidelines make it readily applicable in practice. Code is at https://github.com/MIC-DKFZ/nnActive.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14490.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hunter Heidenreich, Ben Elliott, Olivia Dinica, Yosheb Getachew</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> GutenOCR is a family of grounded OCR front-ends obtained by fine-tuning Qwen2.5-VL-3B and Qwen2.5-VL-7B. The resulting single-checkpoint vision-language models expose reading, detection, and grounding through a unified, prompt-based interface. Trained on business documents, scientific articles, and synthetic grounding data, the models support full-page and localized reading with line- and paragraph-level bounding boxes and conditional ``where is x?'' queries. We introduce a grounded OCR evaluation protocol and show that GutenOCR-7B more than doubles the composite grounded OCR score of its Qwen2.5-VL-7B backbone on 10.5K held-out business and scientific pages (0.40 to 0.82). On Fox and OmniDocBench v1.5, our approach substantially improves region- and line-level OCR as well as text-detection recall, but reveals trade-offs in page-level linearization, color-guided OCR, and formula-heavy layouts.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13677</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14490</guid>
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<pubDate>Tue, 20 Jan 2026 07:29:50 +0000</pubDate>
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<pubDate>Tue, 20 Jan 2026 21:26:15 +0000</pubDate>
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<title>Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning</title>
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<title>FARE: Fast-Slow Agentic Robotic Exploration</title>
|
||||||
<link>https://arxiv.org/abs/2601.13697</link>
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<link>https://arxiv.org/abs/2601.14681</link>
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||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13697.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhihang Yuan, Chengyu Yue, Long Huang, Litu Ou, Lei Shi</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Instruction tuning is a standard paradigm for adapting large language models (LLMs), but modern instruction datasets are large, noisy, and redundant, making full-data fine-tuning costly and often unnecessary. Existing data selection methods either build expensive gradient datastores or assign static scores from a weak proxy, largely ignoring evolving uncertainty, and thus missing a key source of LLM interpretability. We propose GRADFILTERING, an objective-agnostic, uncertainty-aware data selection framework that utilizes a small GPT-2 proxy with a LoRA ensemble and aggregates per-example gradients into a Gradient Signal-to-Noise Ratio (G-SNR) utility. Our method matches or surpasses random subsets and strong baselines in most LLM-as-a-judge evaluations as well as in human assessment. Moreover, GRADFILTERING-selected subsets converge faster than competitive filters under the same compute budget, reflecting the benefit of uncertainty-aware scoring.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14681.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuhao Liao, Xuxin Lv, Jeric Lew, Shizhe Zhang, Jingsong Liang, Peizhuo Li, Yuhong Cao, Wenjun Wu, Guillaume Sartoretti</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration framework that integrates a large language model (LLM) for global reasoning with a reinforcement learning (RL) policy for local decision making. FARE follows a fast-slow thinking paradigm. The slow-thinking LLM module interprets a concise textual description of the unknown environment and synthesizes an agent-level exploration strategy, which is then grounded into a sequence of global waypoints through a topological graph. To further improve reasoning efficiency, this module employs a modularity-based pruning mechanism that reduces redundant graph structures. The fast-thinking RL module executes exploration by reacting to local observations while being guided by the LLM-generated global waypoints. The RL policy is additionally shaped by a reward term that encourages adherence to the global waypoints, enabling coherent and robust closed-loop behavior. This architecture decouples semantic reasoning from geometric decision, allowing each module to operate in its appropriate temporal and spatial scale. In challenging simulated environments, our results show that FARE achieves substantial improvements in exploration efficiency over state-of-the-art baselines. We further deploy FARE on hardware and validate it in complex, large scale 200mtimes130m building environment.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13697</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14681</guid>
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<pubDate>Tue, 20 Jan 2026 07:51:32 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 05:56:24 +0000</pubDate>
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<title>DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution</title>
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<title>Typhoon OCR: Open Vision-Language Model For Thai Document Extraction</title>
|
||||||
<link>https://arxiv.org/abs/2601.13761</link>
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<link>https://arxiv.org/abs/2601.14722</link>
|
||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13761.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shengda Fan, Xuyan Ye, Yankai Lin</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Self-play with large language models has emerged as a promising paradigm for achieving self-improving artificial intelligence. However, existing self-play frameworks often suffer from optimization instability, due to (i) non-stationary objectives induced by solver-dependent reward feedback for the Questioner, and (ii) bootstrapping errors from self-generated pseudo-labels used to supervise the Solver. To mitigate these challenges, we introduce DARC (Decoupled Asymmetric Reasoning Curriculum), a two-stage framework that stabilizes the self-evolution process. First, we train the Questioner to synthesize difficulty-calibrated questions, conditioned on explicit difficulty levels and external corpora. Second, we train the Solver with an asymmetric self-distillation mechanism, where a document-augmented teacher generates high-quality pseudo-labels to supervise the student Solver that lacks document access. Empirical results demonstrate that DARC is model-agnostic, yielding an average improvement of 10.9 points across nine reasoning benchmarks and three backbone models. Moreover, DARC consistently outperforms all baselines and approaches the performance of fully supervised models without relying on human annotations.The code is available at https://github.com/RUCBM/DARC.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14722.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Surapon Nonesung, Natapong Nitarach, Teetouch Jaknamon, Pittawat Taveekitworachai, Kunat Pipatanakul</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Document extraction is a core component of digital workflows, yet existing vision-language models (VLMs) predominantly favor high-resource languages. Thai presents additional challenges due to script complexity from non-latin letters, the absence of explicit word boundaries, and the prevalence of highly unstructured real-world documents, limiting the effectiveness of current open-source models. This paper presents Typhoon OCR, an open VLM for document extraction tailored for Thai and English. The model is fine-tuned from vision-language backbones using a Thai-focused training dataset. The dataset is developed using a multi-stage data construction pipeline that combines traditional OCR, VLM-based restructuring, and curated synthetic data. Typhoon OCR is a unified framework capable of text transcription, layout reconstruction, and document-level structural consistency. The latest iteration of our model, Typhoon OCR V1.5, is a compact and inference-efficient model designed to reduce reliance on metadata and simplify deployment. Comprehensive evaluations across diverse Thai document categories, including financial reports, government forms, books, infographics, and handwritten documents, show that Typhoon OCR achieves performance comparable to or exceeding larger frontier proprietary models, despite substantially lower computational cost. The results demonstrate that open vision-language OCR models can achieve accurate text extraction and layout reconstruction for Thai documents, reaching performance comparable to proprietary systems while remaining lightweight and deployable.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13761</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14722</guid>
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||||||
<pubDate>Tue, 20 Jan 2026 09:12:27 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 07:24:32 +0000</pubDate>
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<title>FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs</title>
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<title>Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning</title>
|
||||||
<link>https://arxiv.org/abs/2601.13836</link>
|
<link>https://arxiv.org/abs/2601.14750</link>
|
||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13836.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qian Chen, Jinlan Fu, Changsong Li, See-Kiong Ng, Xipeng Qiu</p><p><b>Upvotes:</b> 27</p><p><b>Summary:</b> Although Multimodal Large Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplored, as existing benchmarks focus mainly on retrospective understanding. To bridge this gap, we introduce FutureOmni, the first benchmark designed to evaluate omni-modal future forecasting from audio-visual environments. The evaluated models are required to perform cross-modal causal and temporal reasoning, as well as effectively leverage internal knowledge to predict future events. FutureOmni is constructed via a scalable LLM-assisted, human-in-the-loop pipeline and contains 919 videos and 1,034 multiple-choice QA pairs across 8 primary domains. Evaluations on 13 omni-modal and 7 video-only models show that current systems struggle with audio-visual future prediction, particularly in speech-heavy scenarios, with the best accuracy of 64.8% achieved by Gemini 3 Flash. To mitigate this limitation, we curate a 7K-sample instruction-tuning dataset and propose an Omni-Modal Future Forecasting (OFF) training strategy. Evaluations on FutureOmni and popular audio-visual and video-only benchmarks demonstrate that OFF enhances future forecasting and generalization. We publicly release all code (https://github.com/OpenMOSS/FutureOmni) and datasets (https://huggingface.co/datasets/OpenMOSS-Team/FutureOmni).</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14750.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yifan Wang, Shiyu Li, Peiming Li, Xiaochen Yang, Yang Tang, Zheng Wei</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Chain-of-Thought (CoT) prompting has achieved remarkable success in unlocking the reasoning capabilities of Large Language Models (LLMs). Although CoT prompting enhances reasoning, its verbosity imposes substantial computational overhead. Recent works often focus exclusively on outcome alignment and lack supervision on the intermediate reasoning process. These deficiencies obscure the analyzability of the latent reasoning chain. To address these challenges, we introduce Render-of-Thought (RoT), the first framework to reify the reasoning chain by rendering textual steps into images, making the latent rationale explicit and traceable. Specifically, we leverage the vision encoders of existing Vision Language Models (VLMs) as semantic anchors to align the vision embeddings with the textual space. This design ensures plug-and-play implementation without incurring additional pre-training overhead. Extensive experiments on mathematical and logical reasoning benchmarks demonstrate that our method achieves 3-4x token compression and substantial inference acceleration compared to explicit CoT. Furthermore, it maintains competitive performance against other methods, validating the feasibility of this paradigm. Our code is available at https://github.com/TencentBAC/RoT</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.13836</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.14750</guid>
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||||||
<pubDate>Tue, 20 Jan 2026 10:47:20 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 08:09:25 +0000</pubDate>
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<title>FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation</title>
|
<title>The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems</title>
|
||||||
<link>https://arxiv.org/abs/2601.13976</link>
|
<link>https://arxiv.org/abs/2601.15059</link>
|
||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.13976.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jing Zuo, Lingzhou Mu, Fan Jiang, Chengcheng Ma, Mu Xu, Yonggang Qi</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Achieving human-level performance in Vision-and-Language Navigation (VLN) requires an embodied agent to jointly understand multimodal instructions and visual-spatial context while reasoning over long action sequences. Recent works, such as NavCoT and NavGPT-2, demonstrate the potential of Chain-of-Thought (CoT) reasoning for improving interpretability and long-horizon planning. Moreover, multimodal extensions like OctoNav-R1 and CoT-VLA further validate CoT as a promising pathway toward human-like navigation reasoning. However, existing approaches face critical drawbacks: purely textual CoTs lack spatial grounding and easily overfit to sparse annotated reasoning steps, while multimodal CoTs incur severe token inflation by generating imagined visual observations, making real-time navigation impractical. In this work, we propose FantasyVLN, a unified implicit reasoning framework that preserves the benefits of CoT reasoning without explicit token overhead. Specifically, imagined visual tokens are encoded into a compact latent space using a pretrained Visual AutoRegressor (VAR) during CoT reasoning training, and the model jointly learns from textual, visual, and multimodal CoT modes under a unified multi-CoT strategy. At inference, our model performs direct instruction-to-action mapping while still enjoying reasoning-aware representations. Extensive experiments on LH-VLN show that our approach achieves reasoning-aware yet real-time navigation, improving success rates and efficiency while reducing inference latency by an order of magnitude compared to explicit CoT methods.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.15059.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Oleg Romanchuk, Roman Bondar</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.</p></description>
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<pubDate>Tue, 20 Jan 2026 13:54:10 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 15:05:27 +0000</pubDate>
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<title>Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models</title>
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<title>Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek</title>
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<link>https://arxiv.org/abs/2601.14004</link>
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<link>https://arxiv.org/abs/2601.15100</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14004.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hengyuan Zhang, Zhihao Zhang, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Ercong Nie, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schütze, Xuanjing Huang, Qi Zhang, Ngai Wong</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat MI as an observational science, summarizing analytical insights while lacking a systematic framework for actionable intervention. To bridge this gap, we present a practical survey structured around the pipeline: "Locate, Steer, and Improve." We formally categorize Localizing (diagnosis) and Steering (intervention) methods based on specific Interpretable Objects to establish a rigorous intervention protocol. Furthermore, we demonstrate how this framework enables tangible improvements in Alignment, Capability, and Efficiency, effectively operationalizing MI as an actionable methodology for model optimization. The curated paper list of this work is available at https://github.com/rattlesnakey/Awesome-Actionable-MI-Survey.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.15100.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yanwei Huang, Arpit Narechania</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Web AI agents such as ChatGPT Agent and GenSpark are increasingly used for routine web-based tasks, yet they still rely on text-based input prompts, lack proactive detection of user intent, and offer no support for interactive data analysis and decision making. We present WebSeek, a mixed-initiative browser extension that enables users to discover and extract information from webpages to then flexibly build, transform, and refine tangible data artifacts-such as tables, lists, and visualizations-all within an interactive canvas. Within this environment, users can perform analysis-including data transformations such as joining tables or creating visualizations-while an in-built AI both proactively offers context-aware guidance and automation, and reactively responds to explicit user requests. An exploratory user study (N=15) with WebSeek as a probe reveals participants' diverse analysis strategies, underscoring their desire for transparency and control during human-AI collaboration.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.15100</guid>
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<pubDate>Tue, 20 Jan 2026 14:23:23 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 15:38:57 +0000</pubDate>
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<title>PRiSM: Benchmarking Phone Realization in Speech Models</title>
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<title>Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models</title>
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<link>https://arxiv.org/abs/2601.14046</link>
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<link>https://arxiv.org/abs/2601.15220</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14046.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero Jacome, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in developing PR systems, current evaluations only measure surface-level transcription accuracy. We introduce PRiSM, the first open-source benchmark designed to expose blind spots in phonetic perception through intrinsic and extrinsic evaluation of PR systems. PRiSM standardizes transcription-based evaluation and assesses downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. We find that diverse language exposure during training is key to PR performance, encoder-CTC models are the most stable, and specialized PR models still outperform Large Audio Language Models. PRiSM releases code, recipes, and datasets to move the field toward multilingual speech models with robust phonetic ability: https://github.com/changelinglab/prism.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.15220.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Anmol Goel, Cornelius Emde, Sangdoo Yun, Seong Joon Oh, Martin Gubri</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle patterns in training data can degrade contextual privacy, including optimisation for helpfulness, exposure to user information, emotional and subjective dialogue, and debugging code printing internal variables, among others. Fine-tuned models lose their ability to reason about contextual privacy norms, share information inappropriately with tools, and violate memory boundaries across contexts. Privacy collapse is a ``silent failure'' because models maintain high performance on standard safety and utility benchmarks whilst exhibiting severe privacy vulnerabilities. Our experiments show evidence of privacy collapse across six models (closed and open weight), five fine-tuning datasets (real-world and controlled data), and two task categories (agentic and memory-based). Our mechanistic analysis reveals that privacy representations are uniquely fragile to fine-tuning, compared to task-relevant features which are preserved. Our results reveal a critical gap in current safety evaluations, in particular for the deployment of specialised agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.15220</guid>
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<pubDate>Tue, 20 Jan 2026 15:00:36 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 17:53:06 +0000</pubDate>
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<title>Toward Efficient Agents: Memory, Tool learning, and Planning</title>
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<title>Rethinking Video Generation Model for the Embodied World</title>
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<link>https://arxiv.org/abs/2601.14192</link>
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<link>https://arxiv.org/abs/2601.15282</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14192.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiaofang Yang, Lijun Li, Heng Zhou, Tong Zhu, Xiaoye Qu, Yuchen Fan, Qianshan Wei, Rui Ye, Li Kang, Yiran Qin, Zhiqiang Kou, Daizong Liu, Qi Li, Ning Ding, Siheng Chen, Jing Shao</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, which is crucial for real-world deployment, has often been overlooked. This paper therefore investigates efficiency from three core components of agents: memory, tool learning, and planning, considering costs such as latency, tokens, steps, etc. Aimed at conducting comprehensive research addressing the efficiency of the agentic system itself, we review a broad range of recent approaches that differ in implementation yet frequently converge on shared high-level principles including but not limited to bounding context via compression and management, designing reinforcement learning rewards to minimize tool invocation, and employing controlled search mechanisms to enhance efficiency, which we discuss in detail. Accordingly, we characterize efficiency in two complementary ways: comparing effectiveness under a fixed cost budget, and comparing cost at a comparable level of effectiveness. This trade-off can also be viewed through the Pareto frontier between effectiveness and cost. From this perspective, we also examine efficiency oriented benchmarks by summarizing evaluation protocols for these components and consolidating commonly reported efficiency metrics from both benchmark and methodological studies. Moreover, we discuss the key challenges and future directions, with the goal of providing promising insights.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.15282.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yufan Deng, Zilin Pan, Hongyu Zhang, Xiaojie Li, Ruoqing Hu, Yufei Ding, Yiming Zou, Yan Zeng, Daquan Zhou</p><p><b>Upvotes:</b> 36</p><p><b>Summary:</b> Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high-quality videos that accurately reflect real-world robotic interactions remains challenging, and the lack of a standardized benchmark limits fair comparisons and progress. To address this gap, we introduce a comprehensive robotics benchmark, RBench, designed to evaluate robot-oriented video generation across five task domains and four distinct embodiments. It assesses both task-level correctness and visual fidelity through reproducible sub-metrics, including structural consistency, physical plausibility, and action completeness. Evaluation of 25 representative models highlights significant deficiencies in generating physically realistic robot behaviors. Furthermore, the benchmark achieves a Spearman correlation coefficient of 0.96 with human evaluations, validating its effectiveness. While RBench provides the necessary lens to identify these deficiencies, achieving physical realism requires moving beyond evaluation to address the critical shortage of high-quality training data. Driven by these insights, we introduce a refined four-stage data pipeline, resulting in RoVid-X, the largest open-source robotic dataset for video generation with 4 million annotated video clips, covering thousands of tasks and enriched with comprehensive physical property annotations. Collectively, this synergistic ecosystem of evaluation and data establishes a robust foundation for rigorous assessment and scalable training of video models, accelerating the evolution of embodied AI toward general intelligence.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2601.15282</guid>
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<pubDate>Tue, 20 Jan 2026 17:51:56 +0000</pubDate>
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<pubDate>Wed, 21 Jan 2026 18:59:18 +0000</pubDate>
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<title>InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning</title>
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<link>https://arxiv.org/abs/2601.14209</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14209.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Matthew Y. R. Yang, Hao Bai, Ian Wu, Gene Yang, Amrith Setlur, Aviral Kumar</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Outcome-reward reinforcement learning (RL) has proven effective at improving the reasoning capabilities of large language models (LLMs). However, standard RL assigns credit only at the level of the final answer, penalizing entire reasoning traces when the outcome is incorrect and uniformly reinforcing all steps when it is correct. As a result, correct intermediate steps may be discouraged in failed traces, while spurious steps may be reinforced in successful ones. We refer to this failure mode as the problem of credit assignment. While a natural remedy is to train a process reward model, accurately optimizing such models to identify corrective reasoning steps remains challenging. We introduce Intervention Training (InT), a training paradigm in which the model performs fine-grained credit assignment on its own reasoning traces by proposing short, targeted corrections that steer trajectories toward higher reward. Using reference solutions commonly available in mathematical reasoning datasets and exploiting the fact that verifying a model-generated solution is easier than generating a correct one from scratch, the model identifies the first error in its reasoning and proposes a single-step intervention to redirect the trajectory toward the correct solution. We then apply supervised fine-tuning (SFT) to the on-policy rollout up to the point of error concatenated with the intervention, localizing error to the specific step that caused failure. We show that the resulting model serves as a far better initialization for RL training. After running InT and subsequent fine-tuning with RL, we improve accuracy by nearly 14% over a 4B-parameter base model on IMO-AnswerBench, outperforming larger open-source models such as gpt-oss-20b.</p></description>
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<pubDate>Tue, 20 Jan 2026 18:15:38 +0000</pubDate>
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<title>KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2601.14232</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14232.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Egor Cherepanov, Daniil Zelezetsky, Alexey K. Kovalev, Aleksandr I. Panov</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Pixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis. We introduce KAGE-Env, a JAX-native 2D platformer that factorizes the observation process into independently controllable visual axes while keeping the underlying control problem fixed. By construction, varying a visual axis affects performance only through the induced state-conditional action distribution of a pixel policy, providing a clean abstraction for visual generalization. Building on this environment, we define KAGE-Bench, a benchmark of six known-axis suites comprising 34 train-evaluation configuration pairs that isolate individual visual shifts. Using a standard PPO-CNN baseline, we observe strong axis-dependent failures, with background and photometric shifts often collapsing success, while agent-appearance shifts are comparatively benign. Several shifts preserve forward motion while breaking task completion, showing that return alone can obscure generalization failures. Finally, the fully vectorized JAX implementation enables up to 33M environment steps per second on a single GPU, enabling fast and reproducible sweeps over visual factors. Code: https://avanturist322.github.io/KAGEBench/.</p></description>
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<pubDate>Tue, 20 Jan 2026 18:44:28 +0000</pubDate>
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<title>Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment</title>
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<link>https://arxiv.org/abs/2601.14249</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14249.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Long chain-of-thought (CoT) trajectories provide rich supervision signals for distilling reasoning from teacher to student LLMs. However, both prior work and our experiments show that trajectories from stronger teachers do not necessarily yield better students, highlighting the importance of data-student suitability in distillation. Existing methods assess suitability primarily through student likelihood, favoring trajectories that closely align with the model's current behavior but overlooking more informative ones. Addressing this, we propose Rank-Surprisal Ratio (RSR), a simple metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory. RSR is motivated by the observation that effective trajectories typically combine low absolute probability with relatively high-ranked tokens under the student model, balancing learning signal strength and behavioral alignment. Concretely, RSR is defined as the ratio of a trajectory's average token-wise rank to its average negative log-likelihood, and is straightforward to compute and interpret. Across five student models and reasoning trajectories from 11 diverse teachers, RSR strongly correlates with post-training performance (average Spearman 0.86), outperforming existing metrics. We further demonstrate its practical utility in both trajectory selection and teacher selection.</p></description>
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<pubDate>Tue, 20 Jan 2026 18:58:10 +0000</pubDate>
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<title>OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer</title>
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<link>https://arxiv.org/abs/2601.14250</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14250.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Pengze Zhang, Yanze Wu, Mengtian Li, Xu Bai, Songtao Zhao, Fulong Ye, Chong Mou, Xinghui Li, Zhuowei Chen, Qian He, Mingyuan Gao</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Videos convey richer information than images or text, capturing both spatial and temporal dynamics. However, most existing video customization methods rely on reference images or task-specific temporal priors, failing to fully exploit the rich spatio-temporal information inherent in videos, thereby limiting flexibility and generalization in video generation. To address these limitations, we propose OmniTransfer, a unified framework for spatio-temporal video transfer. It leverages multi-view information across frames to enhance appearance consistency and exploits temporal cues to enable fine-grained temporal control. To unify various video transfer tasks, OmniTransfer incorporates three key designs: Task-aware Positional Bias that adaptively leverages reference video information to improve temporal alignment or appearance consistency; Reference-decoupled Causal Learning separating reference and target branches to enable precise reference transfer while improving efficiency; and Task-adaptive Multimodal Alignment using multimodal semantic guidance to dynamically distinguish and tackle different tasks. Extensive experiments show that OmniTransfer outperforms existing methods in appearance (ID and style) and temporal transfer (camera movement and video effects), while matching pose-guided methods in motion transfer without using pose, establishing a new paradigm for flexible, high-fidelity video generation.</p></description>
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<pubDate>Tue, 20 Jan 2026 18:58:11 +0000</pubDate>
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<title>LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR</title>
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<link>https://arxiv.org/abs/2601.14251</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2601.14251.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Said Taghadouini, Adrien Cavaillès, Baptiste Aubertin</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> We present LightOnOCR-2-1B, a 1B-parameter end-to-end multilingual vision--language model that converts document images (e.g., PDFs) into clean, naturally ordered text without brittle OCR pipelines. Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench while being 9times smaller and substantially faster than prior best-performing models. We further extend the output format to predict normalized bounding boxes for embedded images, introducing localization during pretraining via a resume strategy and refining it with RLVR using IoU-based rewards. Finally, we improve robustness with checkpoint averaging and task-arithmetic merging. We release model checkpoints under Apache 2.0, and publicly release the dataset and LightOnOCR-bbox-bench evaluation under their respective licenses.</p></description>
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<pubDate>Tue, 20 Jan 2026 18:58:32 +0000</pubDate>
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Reference in New Issue
Block a user