741 lines
217 KiB
XML
741 lines
217 KiB
XML
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<channel>
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<title>Hugging Face Daily Papers</title>
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<link>https://huggingface.co/papers</link>
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<description>Daily research papers curated by the Hugging Face community.</description>
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<docs>http://www.rssboard.org/rss-specification</docs>
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<generator>python-feedgen</generator>
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<language>en</language>
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<lastBuildDate>Mon, 01 Sep 2025 00:13:30 +0000</lastBuildDate>
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<item>
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<title>Selective Contrastive Learning for Weakly Supervised Affordance Grounding</title>
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<link>https://arxiv.org/abs/2508.07877</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.07877.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> WonJun Moon, Hyun Seok Seong, Jae-Pil Heo</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Facilitating an entity's interaction with objects requires accurately identifying parts that afford specific actions. Weakly supervised affordance grounding (WSAG) seeks to imitate human learning from third-person demonstrations, where humans intuitively grasp functional parts without needing pixel-level annotations. To achieve this, grounding is typically learned using a shared classifier across images from different perspectives, along with distillation strategies incorporating part discovery process. However, since affordance-relevant parts are not always easily distinguishable, models primarily rely on classification, often focusing on common class-specific patterns that are unrelated to affordance. To address this limitation, we move beyond isolated part-level learning by introducing selective prototypical and pixel contrastive objectives that adaptively learn affordance-relevant cues at both the part and object levels, depending on the granularity of the available information. Initially, we find the action-associated objects in both egocentric (object-focused) and exocentric (third-person example) images by leveraging CLIP. Then, by cross-referencing the discovered objects of complementary views, we excavate the precise part-level affordance clues in each perspective. By consistently learning to distinguish affordance-relevant regions from affordance-irrelevant background context, our approach effectively shifts activation from irrelevant areas toward meaningful affordance cues. Experimental results demonstrate the effectiveness of our method. Codes are available at github.com/hynnsk/SelectiveCL.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.07877</guid>
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<pubDate>Mon, 11 Aug 2025 11:49:37 +0000</pubDate>
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</item>
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<item>
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<title>ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks</title>
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<link>https://arxiv.org/abs/2508.08240</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.08240.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Kaijun Wang, Liqin Lu, Mingyu Liu, Jianuo Jiang, Zeju Li, Bolin Zhang, Wancai Zheng, Xinyi Yu, Hao Chen, Chunhua Shen</p><p><b>Upvotes:</b> 43</p><p><b>Summary:</b> Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamental limitations hinder progress: First, although large language models have improved spatial reasoning and task planning through semantic priors, existing implementations remain confined to tabletop scenarios, failing to address the constrained perception and limited actuation ranges of mobile platforms. Second, current manipulation strategies exhibit insufficient generalization when confronted with the diverse object configurations encountered in open-world environments. Third, while crucial for practical deployment, the dual requirement of maintaining high platform maneuverability alongside precise end-effector control in unstructured settings remains understudied. In this work, we present ODYSSEY, a unified mobile manipulation framework for agile quadruped robots equipped with manipulators, which seamlessly integrates high-level task planning with low-level whole-body control. To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model, enabling long-horizon instruction decomposition and precise action execution. At the control level, our novel whole-body policy achieves robust coordination across challenging terrains. We further present the first benchmark for long-horizon mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through successful sim-to-real transfer, we demonstrate the system's generalization and robustness in real-world deployments, underscoring the practicality of legged manipulators in unstructured environments. Our work advances the feasibility of generalized robotic assistants capable of complex, dynamic tasks. Our project page: https://kaijwang.github.io/odyssey.github.io/</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.08240</guid>
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<pubDate>Mon, 11 Aug 2025 17:54:31 +0000</pubDate>
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</item>
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<item>
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<title>ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks</title>
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<link>https://arxiv.org/abs/2508.15804</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15804.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Minghao Li, Ying Zeng, Zhihao Cheng, Cong Ma, Kai Jia</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> The advent of Deep Research agents has substantially reduced the time required for conducting extensive research tasks. However, these tasks inherently demand rigorous standards of factual accuracy and comprehensiveness, necessitating thorough evaluation before widespread adoption. In this paper, we propose ReportBench, a systematic benchmark designed to evaluate the content quality of research reports generated by large language models (LLMs). Our evaluation focuses on two critical dimensions: (1) the quality and relevance of cited literature, and (2) the faithfulness and veracity of the statements within the generated reports. ReportBench leverages high-quality published survey papers available on arXiv as gold-standard references, from which we apply reverse prompt engineering to derive domain-specific prompts and establish a comprehensive evaluation corpus. Furthermore, we develop an agent-based automated framework within ReportBench that systematically analyzes generated reports by extracting citations and statements, checking the faithfulness of cited content against original sources, and validating non-cited claims using web-based resources. Empirical evaluations demonstrate that commercial Deep Research agents such as those developed by OpenAI and Google consistently generate more comprehensive and reliable reports than standalone LLMs augmented with search or browsing tools. However, there remains substantial room for improvement in terms of the breadth and depth of research coverage, as well as factual consistency. The complete code and data will be released at the following link: https://github.com/ByteDance-BandAI/ReportBench</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15804</guid>
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<pubDate>Thu, 14 Aug 2025 03:33:43 +0000</pubDate>
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</item>
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<item>
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<title>Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts</title>
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<link>https://arxiv.org/abs/2508.10390</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.10390.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chiyu Zhang, Lu Zhou, Xiaogang Xu, Jiafei Wu, Liming Fang, Zhe Liu</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Evaluating jailbreak attacks is challenging when prompts are not overtly harmful or fail to induce harmful outputs. Unfortunately, many existing red-teaming datasets contain such unsuitable prompts. To evaluate attacks accurately, these datasets need to be assessed and cleaned for maliciousness. However, existing malicious content detection methods rely on either manual annotation, which is labor-intensive, or large language models (LLMs), which have inconsistent accuracy in harmful types. To balance accuracy and efficiency, we propose a hybrid evaluation framework named MDH (Malicious content Detection based on LLMs with Human assistance) that combines LLM-based annotation with minimal human oversight, and apply it to dataset cleaning and detection of jailbroken responses. Furthermore, we find that well-crafted developer messages can significantly boost jailbreak success, leading us to propose two new strategies: D-Attack, which leverages context simulation, and DH-CoT, which incorporates hijacked chains of thought. The Codes, datasets, judgements, and detection results will be released in github repository: https://github.com/AlienZhang1996/DH-CoT.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.10390</guid>
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<pubDate>Thu, 14 Aug 2025 06:46:56 +0000</pubDate>
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</item>
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<title>EgoTwin: Dreaming Body and View in First Person</title>
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<link>https://arxiv.org/abs/2508.13013</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.13013.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jingqiao Xiu, Fangzhou Hong, Yicong Li, Mengze Li, Wentao Wang, Sirui Han, Liang Pan, Ziwei Liu</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along with camera motion patterns induced by the wearer's body movements. To bridge this gap, we introduce a novel task of joint egocentric video and human motion generation, characterized by two key challenges: 1) Viewpoint Alignment: the camera trajectory in the generated video must accurately align with the head trajectory derived from human motion; 2) Causal Interplay: the synthesized human motion must causally align with the observed visual dynamics across adjacent video frames. To address these challenges, we propose EgoTwin, a joint video-motion generation framework built on the diffusion transformer architecture. Specifically, EgoTwin introduces a head-centric motion representation that anchors the human motion to the head joint and incorporates a cybernetics-inspired interaction mechanism that explicitly captures the causal interplay between video and motion within attention operations. For comprehensive evaluation, we curate a large-scale real-world dataset of synchronized text-video-motion triplets and design novel metrics to assess video-motion consistency. Extensive experiments demonstrate the effectiveness of the EgoTwin framework.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.13013</guid>
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<pubDate>Mon, 18 Aug 2025 15:33:09 +0000</pubDate>
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</item>
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<title>Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics</title>
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<link>https://arxiv.org/abs/2508.13562</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.13562.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuchen Yang, Linfeng Dong, Wei Wang, Zhihang Zhong, Xiao Sun</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> In 3D human pose and shape estimation, SMPLify remains a robust baseline that solves inverse kinematics (IK) through iterative optimization. However, its high computational cost limits its practicality. Recent advances across domains have shown that replacing iterative optimization with data-driven neural networks can achieve significant runtime improvements without sacrificing accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural framework that replaces the iterative fitting process in SMPLify with a single-pass regression model. The design of our framework targets two core challenges in neural IK: data construction and generalization. To enable effective training, we propose a temporal sampling strategy that constructs initialization-target pairs from sequential frames. To improve generalization across diverse motions and unseen poses, we propose a human-centric normalization scheme and residual learning to narrow the solution space. Learnable SMPLify supports both sequential inference and plug-in post-processing to refine existing image-based estimators. Extensive experiments demonstrate that our method establishes itself as a practical and simple baseline: it achieves nearly 200x faster runtime compared to SMPLify, generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic manner when used as a plug-in tool on LucidAction. The code is available at https://github.com/Charrrrrlie/Learnable-SMPLify.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.13562</guid>
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<pubDate>Tue, 19 Aug 2025 06:53:57 +0000</pubDate>
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</item>
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<title>CRISP: Persistent Concept Unlearning via Sparse Autoencoders</title>
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<link>https://arxiv.org/abs/2508.13650</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.13650.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tomer Ashuach, Dana Arad, Aaron Mueller, Martin Tutek, Yonatan Belinkov</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with parameter access. We introduce CRISP, a parameter-efficient method for persistent concept unlearning using SAEs. CRISP automatically identifies salient SAE features across multiple layers and suppresses their activations. We experiment with two LLMs and show that our method outperforms prior approaches on safety-critical unlearning tasks from the WMDP benchmark, successfully removing harmful knowledge while preserving general and in-domain capabilities. Feature-level analysis reveals that CRISP achieves semantically coherent separation between target and benign concepts, allowing precise suppression of the target features.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.13650</guid>
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<pubDate>Tue, 19 Aug 2025 09:01:22 +0000</pubDate>
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</item>
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<title>Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing</title>
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<link>https://arxiv.org/abs/2508.13797</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.13797.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Feng-Lin Liu, Shi-Yang Li, Yan-Pei Cao, Hongbo Fu, Lin Gao</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Recent video editing methods achieve attractive results in style transfer or appearance modification. However, editing the structural content of 3D scenes in videos remains challenging, particularly when dealing with significant viewpoint changes, such as large camera rotations or zooms. Key challenges include generating novel view content that remains consistent with the original video, preserving unedited regions, and translating sparse 2D inputs into realistic 3D video outputs. To address these issues, we propose Sketch3DVE, a sketch-based 3D-aware video editing method to enable detailed local manipulation of videos with significant viewpoint changes. To solve the challenge posed by sparse inputs, we employ image editing methods to generate edited results for the first frame, which are then propagated to the remaining frames of the video. We utilize sketching as an interaction tool for precise geometry control, while other mask-based image editing methods are also supported. To handle viewpoint changes, we perform a detailed analysis and manipulation of the 3D information in the video. Specifically, we utilize a dense stereo method to estimate a point cloud and the camera parameters of the input video. We then propose a point cloud editing approach that uses depth maps to represent the 3D geometry of newly edited components, aligning them effectively with the original 3D scene. To seamlessly merge the newly edited content with the original video while preserving the features of unedited regions, we introduce a 3D-aware mask propagation strategy and employ a video diffusion model to produce realistic edited videos. Extensive experiments demonstrate the superiority of Sketch3DVE in video editing. Homepage and code: http://http://geometrylearning.com/Sketch3DVE/</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.13797</guid>
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<pubDate>Tue, 19 Aug 2025 12:57:31 +0000</pubDate>
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<title>Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR</title>
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<link>https://arxiv.org/abs/2508.14029</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.14029.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiao Liang, Zhongzhi Li, Yeyun Gong, Yelong Shen, Ying Nian Wu, Zhijiang Guo, Weizhu Chen</p><p><b>Upvotes:</b> 115</p><p><b>Summary:</b> Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance at the expense of policy entropy, leading to reduced generation diversity and limiting the Pass@k performance, which typically represents the upper bound of LLM reasoning capability. In this paper, we systematically analyze the policy's generation diversity from the perspective of training problems and find that augmenting and updating training problems helps mitigate entropy collapse during training. Based on these observations, we propose an online Self-play with Variational problem Synthesis (SvS) strategy for RLVR training, which uses the policy's correct solutions to synthesize variational problems while ensuring their reference answers remain identical to the originals. This self-improving strategy effectively maintains policy entropy during training and substantially improves Pass@k compared with standard RLVR, sustaining prolonged improvements and achieving absolute gains of 18.3% and 22.8% in Pass@32 performance on the competition-level AIME24 and AIME25 benchmarks. Experiments on 12 reasoning benchmarks across varying model sizes from 3B to 32B consistently demonstrate the generalizability and robustness of SvS.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.14029</guid>
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<pubDate>Tue, 19 Aug 2025 17:42:45 +0000</pubDate>
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<title>Distilled-3DGS:Distilled 3D Gaussian Splatting</title>
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<link>https://arxiv.org/abs/2508.14037</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.14037.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Lintao Xiang, Xinkai Chen, Jianhuang Lai, Guangcong Wang</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> 3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view synthesis (NVS). However, it suffers from a significant drawback: achieving high-fidelity rendering typically necessitates a large number of 3D Gaussians, resulting in substantial memory consumption and storage requirements. To address this challenge, we propose the first knowledge distillation framework for 3DGS, featuring various teacher models, including vanilla 3DGS, noise-augmented variants, and dropout-regularized versions. The outputs of these teachers are aggregated to guide the optimization of a lightweight student model. To distill the hidden geometric structure, we propose a structural similarity loss to boost the consistency of spatial geometric distributions between the student and teacher model. Through comprehensive quantitative and qualitative evaluations across diverse datasets, the proposed Distilled-3DGS, a simple yet effective framework without bells and whistles, achieves promising rendering results in both rendering quality and storage efficiency compared to state-of-the-art methods. Project page: https://distilled3dgs.github.io . Code: https://github.com/lt-xiang/Distilled-3DGS .</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.14037</guid>
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<pubDate>Tue, 19 Aug 2025 17:59:26 +0000</pubDate>
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<title>Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels</title>
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<link>https://arxiv.org/abs/2508.17437</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17437.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Long Le, Ryan Lucas, Chen Wang, Chuhao Chen, Dinesh Jayaraman, Eric Eaton, Lingjie Liu</p><p><b>Upvotes:</b> 33</p><p><b>Summary:</b> Inferring the physical properties of 3D scenes from visual information is a critical yet challenging task for creating interactive and realistic virtual worlds. While humans intuitively grasp material characteristics such as elasticity or stiffness, existing methods often rely on slow, per-scene optimization, limiting their generalizability and application. To address this problem, we introduce PIXIE, a novel method that trains a generalizable neural network to predict physical properties across multiple scenes from 3D visual features purely using supervised losses. Once trained, our feed-forward network can perform fast inference of plausible material fields, which coupled with a learned static scene representation like Gaussian Splatting enables realistic physics simulation under external forces. To facilitate this research, we also collected PIXIEVERSE, one of the largest known datasets of paired 3D assets and physic material annotations. Extensive evaluations demonstrate that PIXIE is about 1.46-4.39x better and orders of magnitude faster than test-time optimization methods. By leveraging pretrained visual features like CLIP, our method can also zero-shot generalize to real-world scenes despite only ever been trained on synthetic data. https://pixie-3d.github.io/</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17437</guid>
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<pubDate>Wed, 20 Aug 2025 19:24:04 +0000</pubDate>
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<title>CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning</title>
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<link>https://arxiv.org/abs/2508.15868</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15868.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang, Haipang Wu, Yulun Zhang</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinforcement Learning (RL)-based fine-tuning approaches have been proposed to address the limited generalization capability of LLMs trained solely via Supervised Fine-Tuning (SFT). Despite their effectiveness, two major limitations hinder the advancement of LLMs. First, vanilla RL-based approaches ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling, which typically results in model collapse, unstable training process, and suboptimal performance. Second, existing SFT approaches generally overemphasize the annotated CoT, potentially leading to performance degradation due to insufficient exploitation of potential CoT. In this paper, we propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach, i.e., , to enhance the reasoning performance of LLMs while addressing the aforementioned limitations. Specifically, we propose learning a representation for each CoT. Based on this representation, we design novel contrastive signals to guide the fine-tuning process. Our approach not only fully exploits the available annotated CoT but also stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. We conduct comprehensive experiments and in-depth analysis with three baseline approaches, two foundation models, and two datasets to demonstrate significant advantages of in terms of robustness, performance (up to 10.15\%), and efficiency (up to 30.62\%). Code is available at https://github.com/WNQzhu/CARFT.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15868</guid>
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<pubDate>Thu, 21 Aug 2025 00:20:47 +0000</pubDate>
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<title>Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering</title>
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<link>https://arxiv.org/abs/2508.15213</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15213.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bolei He, Xinran He, Run Shao, Shanfu Shu, Xianwei Xue, Mingquan Cheng, Haifeng Li, Zhenhua Ling</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suffers from hallucinations and latency due to noisy retrievals. Continued pretraining internalizes domain knowledge but is costly and lacks cross-domain flexibility. We attribute this challenge to the long-tail distribution of domain knowledge, which leaves partial yet useful internal knowledge underutilized. We further argue that knowledge acquisition should be progressive, mirroring human learning: first understanding concepts, then applying them to complex reasoning. To address this, we propose Selct2Know (S2K), a cost-effective framework that internalizes domain knowledge through an internal-external knowledge self-selection strategy and selective supervised fine-tuning. We also introduce a structured reasoning data generation pipeline and integrate GRPO to enhance reasoning ability. Experiments on medical, legal, and financial QA benchmarks show that S2K consistently outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15213</guid>
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<pubDate>Thu, 21 Aug 2025 03:53:35 +0000</pubDate>
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<title>Collaborative Multi-Modal Coding for High-Quality 3D Generation</title>
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<link>https://arxiv.org/abs/2508.15228</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15228.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ziang Cao, Zhaoxi Chen, Liang Pan, Ziwei Liu</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> 3D content inherently encompasses multi-modal characteristics and can be projected into different modalities (e.g., RGB images, RGBD, and point clouds). Each modality exhibits distinct advantages in 3D asset modeling: RGB images contain vivid 3D textures, whereas point clouds define fine-grained 3D geometries. However, most existing 3D-native generative architectures either operate predominantly within single-modality paradigms-thus overlooking the complementary benefits of multi-modality data-or restrict themselves to 3D structures, thereby limiting the scope of available training datasets. To holistically harness multi-modalities for 3D modeling, we present TriMM, the first feed-forward 3D-native generative model that learns from basic multi-modalities (e.g., RGB, RGBD, and point cloud). Specifically, 1) TriMM first introduces collaborative multi-modal coding, which integrates modality-specific features while preserving their unique representational strengths. 2) Furthermore, auxiliary 2D and 3D supervision are introduced to raise the robustness and performance of multi-modal coding. 3) Based on the embedded multi-modal code, TriMM employs a triplane latent diffusion model to generate 3D assets of superior quality, enhancing both the texture and the geometric detail. Extensive experiments on multiple well-known datasets demonstrate that TriMM, by effectively leveraging multi-modality, achieves competitive performance with models trained on large-scale datasets, despite utilizing a small amount of training data. Furthermore, we conduct additional experiments on recent RGB-D datasets, verifying the feasibility of incorporating other multi-modal datasets into 3D generation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15228</guid>
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<pubDate>Thu, 21 Aug 2025 04:31:14 +0000</pubDate>
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<title>TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill \& Decode Inference</title>
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<link>https://arxiv.org/abs/2508.15881</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15881.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiaojuan Tang, Fanxu Meng, Pingzhi Tang, Yuxuan Wang, Di Yin, Xing Sun, Muhan Zhang</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value states into a low-rank latent vector, caching only this vector to reduce memory. In tensor parallelism (TP), however, attention heads are computed across multiple devices, and each device must load the full cache, eroding the advantage of MLA over Grouped Query Attention (GQA). We propose Tensor-Parallel Latent Attention (TPLA): a scheme that partitions both the latent representation and each head's input dimension across devices, performs attention independently per shard, and then combines results with an all-reduce. TPLA preserves the benefits of a compressed KV cache while unlocking TP efficiency. Unlike Grouped Latent Attention (GLA), every head in TPLA still leverages the full latent representation, maintaining stronger representational capacity. TPLA is drop-in compatible with models pre-trained using MLA: it supports MLA-style prefilling and enables efficient tensor-parallel decoding without retraining. Applying simple orthogonal transforms -- e.g., the Hadamard transform or PCA -- before TP slicing further mitigates cross-shard interference, yielding minimal accuracy degradation. By reducing the per-device KV cache for DeepSeek-V3 and Kimi-K2, we achieve 1.79x and 1.93x speedups, respectively, at a 32K-token context length while maintaining performance on commonsense and LongBench benchmarks. TPLA can be implemented with FlashAttention-3, enabling practical end-to-end acceleration.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15881</guid>
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<pubDate>Thu, 21 Aug 2025 15:25:40 +0000</pubDate>
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<title>Beyond Transcription: Mechanistic Interpretability in ASR</title>
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<link>https://arxiv.org/abs/2508.15882</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15882.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Neta Glazer, Yael Segal-Feldman, Hilit Segev, Aviv Shamsian, Asaf Buchnick, Gill Hetz, Ethan Fetaya, Joseph Keshet, Aviv Navon</p><p><b>Upvotes:</b> 80</p><p><b>Summary:</b> Interpretability methods have recently gained significant attention, particularly in the context of large language models, enabling insights into linguistic representations, error detection, and model behaviors such as hallucinations and repetitions. However, these techniques remain underexplored in automatic speech recognition (ASR), despite their potential to advance both the performance and interpretability of ASR systems. In this work, we adapt and systematically apply established interpretability methods such as logit lens, linear probing, and activation patching, to examine how acoustic and semantic information evolves across layers in ASR systems. Our experiments reveal previously unknown internal dynamics, including specific encoder-decoder interactions responsible for repetition hallucinations and semantic biases encoded deep within acoustic representations. These insights demonstrate the benefits of extending and applying interpretability techniques to speech recognition, opening promising directions for future research on improving model transparency and robustness.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15882</guid>
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<pubDate>Thu, 21 Aug 2025 15:42:53 +0000</pubDate>
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<title>End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning</title>
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<link>https://arxiv.org/abs/2508.15746</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15746.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qiaoyu Zheng, Yuze Sun, Chaoyi Wu, Weike Zhao, Pengcheng Qiu, Yongguo Yu, Kun Sun, Yanfeng Wang, Ya Zhang, Weidi Xie</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Accurate diagnosis with medical large language models is hindered by knowledge gaps and hallucinations. Retrieval and tool-augmented methods help, but their impact is limited by weak use of external knowledge and poor feedback-reasoning traceability. To address these challenges, We introduce Deep-DxSearch, an agentic RAG system trained end-to-end with reinforcement learning (RL) that enables steer tracebale retrieval-augmented reasoning for medical diagnosis. In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources to support retrieval-aware reasoning across diagnostic scenarios. More crutially, we frame the LLM as the core agent and the retrieval corpus as its environment, using tailored rewards on format, retrieval, reasoning structure, and diagnostic accuracy, thereby evolving the agentic RAG policy from large-scale data through RL. Experiments demonstrate that our end-to-end agentic RL training framework consistently outperforms prompt-engineering and training-free RAG approaches across multiple data centers. After training, Deep-DxSearch achieves substantial gains in diagnostic accuracy, surpassing strong diagnostic baselines such as GPT-4o, DeepSeek-R1, and other medical-specific frameworks for both common and rare disease diagnosis under in-distribution and out-of-distribution settings. Moreover, ablation studies on reward design and retrieval corpus components confirm their critical roles, underscoring the uniqueness and effectiveness of our approach compared with traditional implementations. Finally, case studies and interpretability analyses highlight improvements in Deep-DxSearch's diagnostic policy, providing deeper insight into its performance gains and supporting clinicians in delivering more reliable and precise preliminary diagnoses. See https://github.com/MAGIC-AI4Med/Deep-DxSearch.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15746</guid>
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<pubDate>Thu, 21 Aug 2025 17:42:47 +0000</pubDate>
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<title>CineScale: Free Lunch in High-Resolution Cinematic Visual Generation</title>
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<link>https://arxiv.org/abs/2508.15774</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.15774.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haonan Qiu, Ning Yu, Ziqi Huang, Paul Debevec, Ziwei Liu</p><p><b>Upvotes:</b> 19</p><p><b>Summary:</b> Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data and constrained computation resources, hampering their ability to generate high-fidelity images or videos at higher resolutions. Recent efforts have explored tuning-free strategies to exhibit the untapped potential higher-resolution visual generation of pre-trained models. However, these methods are still prone to producing low-quality visual content with repetitive patterns. The key obstacle lies in the inevitable increase in high-frequency information when the model generates visual content exceeding its training resolution, leading to undesirable repetitive patterns deriving from the accumulated errors. In this work, we propose CineScale, a novel inference paradigm to enable higher-resolution visual generation. To tackle the various issues introduced by the two types of video generation architectures, we propose dedicated variants tailored to each. Unlike existing baseline methods that are confined to high-resolution T2I and T2V generation, CineScale broadens the scope by enabling high-resolution I2V and V2V synthesis, built atop state-of-the-art open-source video generation frameworks. Extensive experiments validate the superiority of our paradigm in extending the capabilities of higher-resolution visual generation for both image and video models. Remarkably, our approach enables 8k image generation without any fine-tuning, and achieves 4k video generation with only minimal LoRA fine-tuning. Generated video samples are available at our website: https://eyeline-labs.github.io/CineScale/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.15774</guid>
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<pubDate>Thu, 21 Aug 2025 17:59:57 +0000</pubDate>
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<title>QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting</title>
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<link>https://arxiv.org/abs/2508.16697</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16697.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Nicole Cho, William Watson, Alec Koppel, Sumitra Ganesh, Manuela Veloso</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Advanced reasoning capabilities in Large Language Models (LLMs) have caused higher hallucination prevalence; yet most mitigation work focuses on after-the-fact filtering rather than shaping the queries that trigger them. We introduce QueryBandits, a bandit framework that designs rewrite strategies to maximize a reward model, that encapsulates hallucination propensity based upon the sensitivities of 17 linguistic features of the input query-and therefore, proactively steer LLMs away from generating hallucinations. Across 13 diverse QA benchmarks and 1,050 lexically perturbed queries per dataset, our top contextual QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a no-rewrite baseline and also outperforms zero-shot static prompting ("paraphrase" or "expand") by 42.6% and 60.3% respectively. Therefore, we empirically substantiate the effectiveness of QueryBandits in mitigating hallucination via the intervention that takes the form of a query rewrite. Interestingly, certain static prompting strategies, which constitute a considerable number of current query rewriting literature, have a higher cumulative regret than the no-rewrite baseline, signifying that static rewrites can worsen hallucination. Moreover, we discover that the converged per-arm regression feature weight vectors substantiate that there is no single rewrite strategy optimal for all queries. In this context, guided rewriting via exploiting semantic features with QueryBandits can induce significant shifts in output behavior through forward-pass mechanisms, bypassing the need for retraining or gradient-based adaptation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16697</guid>
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<pubDate>Fri, 22 Aug 2025 01:41:49 +0000</pubDate>
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<title>Training a Foundation Model for Materials on a Budget</title>
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<link>https://arxiv.org/abs/2508.16067</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16067.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Teddy Koker, Tess Smidt</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Foundation models for materials modeling are advancing quickly, but their training remains expensive, often placing state-of-the-art methods out of reach for many research groups. We introduce Nequix, a compact E(3)-equivariant potential that pairs a simplified NequIP design with modern training practices, including equivariant root-mean-square layer normalization and the Muon optimizer, to retain accuracy while substantially reducing compute requirements. Built in JAX, Nequix has 700K parameters and was trained in 500 A100-GPU hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix ranks third overall while requiring less than one quarter of the training cost of most other methods, and it delivers an order-of-magnitude faster inference speed than the current top-ranked model. We release model weights and fully reproducible codebase at https://github.com/atomicarchitects/nequix</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16067</guid>
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<pubDate>Fri, 22 Aug 2025 03:38:06 +0000</pubDate>
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<title>InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles</title>
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<link>https://arxiv.org/abs/2508.16072</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16072.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zizhen Li, Chuanhao Li, Yibin Wang, Qi Chen, Diping Song, Yukang Feng, Jianwen Sun, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Kaipeng Zhang</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditions. To address this, we introduce InMind, a cognitively grounded evaluation framework designed to assess whether LLMs can capture and apply personalized reasoning styles in SDGs. InMind enhances structured gameplay data with round-level strategy traces and post-game reflections, collected under both Observer and Participant modes. It supports four cognitively motivated tasks that jointly evaluate both static alignment and dynamic adaptation. As a case study, we apply InMind to the game Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o frequently rely on lexical cues, struggling to anchor reflections in temporal gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These findings reveal key limitations in current LLMs' capacity for individualized, adaptive reasoning, and position InMind as a step toward cognitively aligned human-AI interaction.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16072</guid>
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<pubDate>Fri, 22 Aug 2025 04:04:00 +0000</pubDate>
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<title>AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs</title>
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<link>https://arxiv.org/abs/2508.16153</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16153.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Huichi Zhou, Yihang Chen, Siyuan Guo, Xue Yan, Kin Hei Lee, Zihan Wang, Ka Yiu Lee, Guchun Zhang, Kun Shao, Linyi Yang, Jun Wang</p><p><b>Upvotes:</b> 115</p><p><b>Summary:</b> In this paper, we introduce a novel learning paradigm for adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approaches are often either rigid, relying on static, handcrafted reflection workflows, or computationally intensive, requiring gradient updates of LLM model parameters. In contrast, our method enables low-cost continual adaptation via memory-based online reinforcement learning. We formalise this as a Memory-augmented Markov Decision Process (M-MDP), equipped with a neural case-selection policy to guide action decisions. Past experiences are stored in an episodic memory, either differentiable or non-parametric. The policy is continually updated based on environmental feedback through a memory rewriting mechanism, whereas policy improvement is achieved through efficient memory reading (retrieval). We instantiate our agent model in the deep research setting, namely AgentFly, which attains top-1 on GAIA validation (87.88% Pass@3) and 79.40% on the test set. It reaches 66.6% F1 and 80.4% PM on the DeepResearcher dataset, outperforming the state-of-the-art training-based method, while case-based memory adds 4.7% to 9.6% absolute points on out-of-distribution tasks. Our approach offers a scalable and efficient pathway for developing generalist LLM agents capable of continuous, real-time learning without gradient updates, advancing machine learning towards open-ended skill acquisition and deep research scenarios. The code is available at https://github.com/Agent-on-the-Fly/AgentFly.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16153</guid>
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<pubDate>Fri, 22 Aug 2025 07:25:30 +0000</pubDate>
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<title>AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications</title>
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<link>https://arxiv.org/abs/2508.16279</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16279.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dawei Gao, Zitao Li, Yuexiang Xie, Weirui Kuang, Liuyi Yao, Bingchen Qian, Zhijian Ma, Yue Cui, Haohao Luo, Shen Li, Lu Yi, Yi Yu, Shiqi He, Zhiling Luo, Wenmeng Zhou, Zhicheng Zhang, Xuguang He, Ziqian Chen, Weikai Liao, Farruh Isakulovich Kushnazarov, Yaliang Li, Bolin Ding, Jingren Zhou</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agentic applications and provide unified interfaces and extensible modules, enabling developers to easily leverage the latest progress, such as new models and MCPs. Furthermore, we ground agent behaviors in the ReAct paradigm and offer advanced agent-level infrastructure based on a systematic asynchronous design, which enriches both human-agent and agent-agent interaction patterns while improving execution efficiency. Building on this foundation, we integrate several built-in agents tailored to specific practical scenarios. AgentScope also includes robust engineering support for developer-friendly experiences. We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace. In addition, AgentScope offers a runtime sandbox to ensure safe agent execution and facilitates rapid deployment in production environments. With these enhancements, AgentScope provides a practical foundation for building scalable, adaptive, and effective agentic applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16279</guid>
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<pubDate>Fri, 22 Aug 2025 10:35:56 +0000</pubDate>
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<title>Do What? Teaching Vision-Language-Action Models to Reject the Impossible</title>
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<link>https://arxiv.org/abs/2508.16292</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16292.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wen-Han Hsieh, Elvis Hsieh, Dantong Niu, Trevor Darrell, Roei Herzig, David M. Chan</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or conditions absent from the environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines, while increasing successful responses in false-premise scenarios by 50.78%.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16292</guid>
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<pubDate>Fri, 22 Aug 2025 10:54:33 +0000</pubDate>
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<title>RotaTouille: Rotation Equivariant Deep Learning for Contours</title>
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<link>https://arxiv.org/abs/2508.16359</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16359.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Odin Hoff Gardaa, Nello Blaser</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16359</guid>
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<pubDate>Fri, 22 Aug 2025 13:05:55 +0000</pubDate>
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<title>AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions</title>
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<link>https://arxiv.org/abs/2508.16402</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16402.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zihan Wang, Jiaze Chen, Zhicheng Liu, Markus Mak, Yidi Du, Geonsik Moon, Luoqi Xu, Aaron Tua, Kunshuo Peng, Jiayi Lu, Mingfei Xia, Boqian Zou, Chenyang Ran, Guang Tian, Shoutai Zhu, Yeheng Duan, Zhenghui Kang, Zhenxing Lin, Shangshu Li, Qiang Luo, Qingshen Long, Zhiyong Chen, Yihan Xiao, Yurong Wu, Daoguang Zan, Yuyi Fu, Mingxuan Wang, Ming Ding</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on existing benchmarks, we argue that current evaluations overstate model proficiency, masking a substantial gap between LLMs and elite human programmers. This gap arises from two key limitations: insufficient difficulty and scope of benchmark problems, and evaluation bias from low-quality test cases. To address these shortcomings, we present AetherCode, a new benchmark that draws problems from premier programming competitions such as IOI and ICPC, offering broader coverage and higher difficulty. AetherCode further incorporates comprehensive, expert-validated test suites built through a hybrid of automated generation and human curation, ensuring rigorous and reliable assessment. By combining challenging problem design with robust evaluation, AetherCode provides a more faithful measure of LLM capabilities and sets a new standard for future research in code reasoning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16402</guid>
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<pubDate>Fri, 22 Aug 2025 14:04:55 +0000</pubDate>
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<title>MV-RAG: Retrieval Augmented Multiview Diffusion</title>
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<link>https://arxiv.org/abs/2508.16577</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16577.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yosef Dayani, Omer Benishu, Sagie Benaim</p><p><b>Upvotes:</b> 34</p><p><b>Summary:</b> Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding inconsistent or inaccurate results. To this end, we propose MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images from a large in-the-wild 2D database and then conditions a multiview diffusion model on these images to synthesize consistent and accurate multiview outputs. Training such a retrieval-conditioned model is achieved via a novel hybrid strategy bridging structured multiview data and diverse 2D image collections. This involves training on multiview data using augmented conditioning views that simulate retrieval variance for view-specific reconstruction, alongside training on sets of retrieved real-world 2D images using a distinctive held-out view prediction objective: the model predicts the held-out view from the other views to infer 3D consistency from 2D data. To facilitate a rigorous OOD evaluation, we introduce a new collection of challenging OOD prompts. Experiments against state-of-the-art text-to-3D, image-to-3D, and personalization baselines show that our approach significantly improves 3D consistency, photorealism, and text adherence for OOD/rare concepts, while maintaining competitive performance on standard benchmarks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16577</guid>
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<pubDate>Fri, 22 Aug 2025 17:59:40 +0000</pubDate>
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<title>Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling</title>
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<link>https://arxiv.org/abs/2508.16745</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16745.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ivan Rodkin, Daniil Orel, Konstantin Smirnov, Arman Bolatov, Bilal Elbouardi, Besher Hassan, Yuri Kuratov, Aydar Bulatov, Preslav Nakov, Timothy Baldwin, Artem Shelmanov, Mikhail Burtsev</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Reasoning is a core capability of large language models, yet understanding how they learn and perform multi-step reasoning remains an open problem. In this study, we explore how different architectures and training methods affect model multi-step reasoning capabilities within a cellular automata framework. By training on state sequences generated with random Boolean functions for random initial conditions to exclude memorization, we demonstrate that most neural architectures learn to abstract the underlying rules. While models achieve high accuracy in next-state prediction, their performance declines sharply if multi-step reasoning is required. We confirm that increasing model depth plays a crucial role for sequential computations. We demonstrate that an extension of the effective model depth with recurrence, memory, and test-time compute scaling substantially enhances reasoning capabilities.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16745</guid>
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<pubDate>Fri, 22 Aug 2025 18:57:08 +0000</pubDate>
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<title>TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling</title>
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<link>https://arxiv.org/abs/2508.16790</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16790.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuancheng Wang, Dekun Chen, Xueyao Zhang, Junan Zhang, Jiaqi Li, Zhizheng Wu</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vector quantization structures or high frame rates, 2) reliance on auxiliary pre-trained models for semantic distillation, and 3) requirements for complex two-stage training processes. In this work, we introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a novel approach designed to overcome these challenges. TaDiCodec employs end-to-end optimization for quantization and reconstruction through a diffusion autoencoder, while integrating text guidance into the diffusion decoder to enhance reconstruction quality and achieve optimal compression. TaDiCodec achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of 0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining superior performance on critical speech generation evaluation metrics such as Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS). Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and obviating the need for auxiliary pre-trained models. We also validate the compatibility of TaDiCodec in language model based zero-shot text-to-speech with both autoregressive modeling and masked generative modeling, demonstrating its effectiveness and efficiency for speech language modeling, as well as a significantly small reconstruction-generation gap. We will open source our code and model checkpoints. Audio samples are are available at https:/tadicodec.github.io/. We release code and model checkpoints at https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16790</guid>
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<pubDate>Fri, 22 Aug 2025 20:45:03 +0000</pubDate>
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<title>If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition</title>
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<link>https://arxiv.org/abs/2508.16838</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16838.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shubhashis Roy Dipta, Francis Ferraro</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as 3-6%. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a 2-5% improvement.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16838</guid>
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<pubDate>Fri, 22 Aug 2025 23:34:24 +0000</pubDate>
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<title>Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning</title>
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<link>https://arxiv.org/abs/2508.16949</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.16949.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yang Zhou, Sunzhu Li, Shunyu Liu, Wenkai Fang, Jiale Zhao, Jingwen Yang, Jianwei Lv, Kongcheng Zhang, Yihe Zhou, Hengtong Lu, Wei Chen, Yan Xie, Mingli Song</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the encouraging results, a fundamental dilemma persists as RL improvement relies on learning from high-quality samples, yet the exploration for such samples remains bounded by the inherent limitations of LLMs. This, in effect, creates an undesirable cycle in which what cannot be explored cannot be learned. In this work, we propose Rubric-Scaffolded Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework designed to break the exploration bottleneck for general LLM reasoning. Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit scaffolding for exploration during rollout generation, where different rubrics are provided as external guidance within task instructions to steer diverse high-quality responses. This guidance is gradually decayed over time, encouraging the model to internalize the underlying reasoning patterns; (2) verifiable rewards for exploitation during model training, where we can obtain robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL on general reasoning tasks. Extensive experiments demonstrate the superiority of the proposed RuscaRL across various benchmarks, effectively expanding reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500, surpassing GPT-4.1. Furthermore, our fine-tuned variant on Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading LLMs including OpenAI-o3.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.16949</guid>
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<pubDate>Sat, 23 Aug 2025 08:47:31 +0000</pubDate>
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<title>REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework</title>
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<link>https://arxiv.org/abs/2508.17061</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17061.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Stefanos Pasios, Nikos Nikolaidis</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Photorealism is an important aspect of modern video games since it can shape the player experience and simultaneously impact the immersion, narrative engagement, and visual fidelity. Although recent hardware technological breakthroughs, along with state-of-the-art rendering technologies, have significantly improved the visual realism of video games, achieving true photorealism in dynamic environments at real-time frame rates still remains a major challenge due to the tradeoff between visual quality and performance. In this short paper, we present a novel approach for enhancing the photorealism of rendered game frames using generative adversarial networks. To this end, we propose Real-time photorealism Enhancement in Games via a dual-stage gEnerative Network framework (REGEN), which employs a robust unpaired image-to-image translation model to produce semantically consistent photorealistic frames that transform the problem into a simpler paired image-to-image translation task. This enables training with a lightweight method that can achieve real-time inference time without compromising visual quality. We demonstrate the effectiveness of our framework on Grand Theft Auto V, showing that the approach achieves visual results comparable to the ones produced by the robust unpaired Im2Im method while improving inference speed by 32.14 times. Our findings also indicate that the results outperform the photorealism-enhanced frames produced by directly training a lightweight unpaired Im2Im translation method to translate the video game frames towards the visual characteristics of real-world images. Code, pre-trained models, and demos for this work are available at: https://github.com/stefanos50/REGEN.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17061</guid>
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<pubDate>Sat, 23 Aug 2025 15:28:05 +0000</pubDate>
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<title>PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs</title>
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<link>https://arxiv.org/abs/2508.17188</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17188.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhilin Zhang, Xiang Zhang, Jiaqi Wei, Yiwei Xu, Chenyu You</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for conferences. While recent approaches have attempted to automate this task, most neglect core design and aesthetic principles, resulting in posters that require substantial manual refinement. To address these design limitations, we propose PosterGen, a multi-agent framework that mirrors the workflow of professional poster designers. It consists of four collaborative specialized agents: (1) Parser and Curator agents extract content from the paper and organize storyboard; (2) Layout agent maps the content into a coherent spatial layout; (3) Stylist agents apply visual design elements such as color and typography; and (4) Renderer composes the final poster. Together, these agents produce posters that are both semantically grounded and visually appealing. To evaluate design quality, we introduce a vision-language model (VLM)-based rubric that measures layout balance, readability, and aesthetic coherence. Experimental results show that PosterGen consistently matches in content fidelity, and significantly outperforms existing methods in visual designs, generating posters that are presentation-ready with minimal human refinements.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17188</guid>
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<pubDate>Sun, 24 Aug 2025 02:25:45 +0000</pubDate>
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<title>ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation</title>
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<link>https://arxiv.org/abs/2508.17234</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17234.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Siying Zhou, Yiquan Wu, Hui Chen, Xavier Hu, Kun Kuang, Adam Jatowt, Ming Hu, Chunyan Zheng, Fei Wu</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Legal claims refer to the plaintiff's demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case's facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17234</guid>
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<pubDate>Sun, 24 Aug 2025 07:19:25 +0000</pubDate>
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<title>MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment</title>
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<link>https://arxiv.org/abs/2508.17290</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17290.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Omid Ghahroodi, Arshia Hemmat, Marzia Nouri, Seyed Mohammad Hadi Hosseini, Doratossadat Dastgheib, Mohammad Vali Sanian, Alireza Sahebi, Reihaneh Zohrabi, Mohammad Hossein Rohban, Ehsaneddin Asgari, Mahdieh Soleymani Baghshah</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model's ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17290</guid>
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<pubDate>Sun, 24 Aug 2025 10:32:37 +0000</pubDate>
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<title>Explain Before You Answer: A Survey on Compositional Visual Reasoning</title>
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<link>https://arxiv.org/abs/2508.17298</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17298.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fucai Ke, Joy Hsu, Zhixi Cai, Zixian Ma, Xin Zheng, Xindi Wu, Sukai Huang, Weiqing Wang, Pari Delir Haghighi, Gholamreza Haffari, Ranjay Krishna, Jiajun Wu, Hamid Rezatofighi</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17298</guid>
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<pubDate>Sun, 24 Aug 2025 11:01:51 +0000</pubDate>
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<title>Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing</title>
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<link>https://arxiv.org/abs/2508.17326</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17326.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tristan S. W. Stevens, Oisín Nolan, Ruud J. G. van Sloun</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, derived from semantic segmentation of hazy inputs into a diffusion posterior sampling framework guided by a generative prior trained on clean ultrasound data. Quantitative evaluation on the challenge dataset demonstrates strong performance across contrast and fidelity metrics. Code for the submitted algorithm is available at https://github.com/tristan-deep/semantic-diffusion-echo-dehazing.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17326</guid>
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<pubDate>Sun, 24 Aug 2025 12:20:18 +0000</pubDate>
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<title>TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling</title>
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<link>https://arxiv.org/abs/2508.17445</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17445.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yizhi Li, Qingshui Gu, Zhoufutu Wen, Ziniu Li, Tianshun Xing, Shuyue Guo, Tianyu Zheng, Xin Zhou, Xingwei Qu, Wangchunshu Zhou, Zheng Zhang, Wei Shen, Qian Liu, Chenghua Lin, Jian Yang, Ge Zhang, Wenhao Huang</p><p><b>Upvotes:</b> 74</p><p><b>Summary:</b> Recent advancements in aligning large language models via reinforcement learning have achieved remarkable gains in solving complex reasoning problems, but at the cost of expensive on-policy rollouts and limited exploration of diverse reasoning paths. In this work, we introduce TreePO, involving a self-guided rollout algorithm that views sequence generation as a tree-structured searching process. Composed of dynamic tree sampling policy and fixed-length segment decoding, TreePO leverages local uncertainty to warrant additional branches. By amortizing computation across common prefixes and pruning low-value paths early, TreePO essentially reduces the per-update compute burden while preserving or enhancing exploration diversity. Key contributions include: (1) a segment-wise sampling algorithm that alleviates the KV cache burden through contiguous segments and spawns new branches along with an early-stop mechanism; (2) a tree-based segment-level advantage estimation that considers both global and local proximal policy optimization. and (3) analysis on the effectiveness of probability and quality-driven dynamic divergence and fallback strategy. We empirically validate the performance gain of TreePO on a set reasoning benchmarks and the efficiency saving of GPU hours from 22\% up to 43\% of the sampling design for the trained models, meanwhile showing up to 40\% reduction at trajectory-level and 35\% at token-level sampling compute for the existing models. While offering a free lunch of inference efficiency, TreePO reveals a practical path toward scaling RL-based post-training with fewer samples and less compute. Home page locates at https://m-a-p.ai/TreePO.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17445</guid>
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<pubDate>Sun, 24 Aug 2025 16:52:37 +0000</pubDate>
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<title>Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD</title>
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<link>https://arxiv.org/abs/2508.17450</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17450.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bryan Chen Zhengyu Tan, Daniel Wai Kit Chin, Zhengyuan Liu, Nancy F. Chen, Roy Ka-Wei Lee</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable deployment. We introduce DuET-PD (Dual Evaluation for Trust in Persuasive Dialogues), a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type (corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions. Moreover, results reveal a concerning trend of increasing sycophancy in newer open-source models. To address this, we introduce Holistic DPO, a training approach balancing positive and negative persuasion examples. Unlike prompting or resist-only training, Holistic DPO enhances both robustness to misinformation and receptiveness to corrections, improving Llama-3.1-8B-Instruct's accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. These contributions offer a pathway to developing more reliable and adaptable LLMs for multi-turn dialogue. Code is available at https://github.com/Social-AI-Studio/DuET-PD.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17450</guid>
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<pubDate>Sun, 24 Aug 2025 17:08:37 +0000</pubDate>
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<title>T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation</title>
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<link>https://arxiv.org/abs/2508.17472</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17472.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Kaiyue Sun, Rongyao Fang, Chengqi Duan, Xian Liu, Xihui Liu</p><p><b>Upvotes:</b> 25</p><p><b>Summary:</b> We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17472</guid>
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<pubDate>Sun, 24 Aug 2025 17:59:38 +0000</pubDate>
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<title>Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice</title>
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<link>https://arxiv.org/abs/2508.17502</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17502.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hugo Bohy, Minh Tran, Kevin El Haddad, Thierry Dutoit, Mohammad Soleymani</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Human social behaviors are inherently multimodal necessitating the development of powerful audiovisual models for their perception. In this paper, we present Social-MAE, our pre-trained audiovisual Masked Autoencoder based on an extended version of Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE), which is pre-trained on audiovisual social data. Specifically, we modify CAV-MAE to receive a larger number of frames as input and pre-train it on a large dataset of human social interaction (VoxCeleb2) in a self-supervised manner. We demonstrate the effectiveness of this model by finetuning and evaluating the model on different social and affective downstream tasks, namely, emotion recognition, laughter detection and apparent personality estimation. The model achieves state-of-the-art results on multimodal emotion recognition and laughter recognition and competitive results for apparent personality estimation, demonstrating the effectiveness of in-domain self-supervised pre-training. Code and model weight are available here https://github.com/HuBohy/SocialMAE.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17502</guid>
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<pubDate>Sun, 24 Aug 2025 19:49:48 +0000</pubDate>
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<title>UQ: Assessing Language Models on Unsolved Questions</title>
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<link>https://arxiv.org/abs/2508.17580</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17580.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fan Nie, Ken Ziyu Liu, Zihao Wang, Rui Sun, Wei Liu, Weijia Shi, Huaxiu Yao, Linjun Zhang, Andrew Y. Ng, James Zou, Sanmi Koyejo, Yejin Choi, Percy Liang, Niklas Muennighoff</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usage. Yet, current paradigms face a difficulty-realism tension: exam-style benchmarks are often made artificially difficult with limited real-world value, while benchmarks based on real user interaction often skew toward easy, high-frequency problems. In this work, we explore a radically different paradigm: assessing models on unsolved questions. Rather than a static benchmark scored once, we curate unsolved questions and evaluate models asynchronously over time with validator-assisted screening and community verification. We introduce UQ, a testbed of 500 challenging, diverse questions sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi and history, probing capabilities including reasoning, factuality, and browsing. UQ is difficult and realistic by construction: unsolved questions are often hard and naturally arise when humans seek answers, thus solving them yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset and its collection pipeline combining rule-based filters, LLM judges, and human review to ensure question quality (e.g., well-defined and difficult); (2) UQ-Validators, compound validation strategies that leverage the generator-validator gap to provide evaluation signals and pre-screen candidate solutions for human review; and (3) UQ-Platform, an open platform where experts collectively verify questions and solutions. The top model passes UQ-validation on only 15% of questions, and preliminary human verification has already identified correct answers among those that passed. UQ charts a path for evaluating frontier models on real-world, open-ended challenges, where success pushes the frontier of human knowledge. We release UQ at https://uq.stanford.edu.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17580</guid>
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<pubDate>Mon, 25 Aug 2025 01:07:59 +0000</pubDate>
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<title>Steering When Necessary: Flexible Steering Large Language Models with Backtracking</title>
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<link>https://arxiv.org/abs/2508.17621</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17621.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jinwei Gan, Zifeng Cheng, Zhiwei Jiang, Cong Wang, Yafeng Yin, Xiang Luo, Yuchen Fu, Qing Gu</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Large language models (LLMs) have achieved remarkable performance across many generation tasks. Nevertheless, effectively aligning them with desired behaviors remains a significant challenge. Activation steering is an effective and cost-efficient approach that directly modifies the activations of LLMs during the inference stage, aligning their responses with the desired behaviors and avoiding the high cost of fine-tuning. Existing methods typically indiscriminately intervene to all generations or rely solely on the question to determine intervention, which limits the accurate assessment of the intervention strength. To this end, we propose the Flexible Activation Steering with Backtracking (FASB) framework, which dynamically determines both the necessity and strength of intervention by tracking the internal states of the LLMs during generation, considering both the question and the generated content. Since intervening after detecting a deviation from the desired behavior is often too late, we further propose the backtracking mechanism to correct the deviated tokens and steer the LLMs toward the desired behavior. Extensive experiments on the TruthfulQA dataset and six multiple-choice datasets demonstrate that our method outperforms baselines. Our code will be released at https://github.com/gjw185/FASB.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17621</guid>
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<pubDate>Mon, 25 Aug 2025 03:01:30 +0000</pubDate>
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<title>Spacer: Towards Engineered Scientific Inspiration</title>
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<link>https://arxiv.org/abs/2508.17661</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17661.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Minhyeong Lee, Suyoung Hwang, Seunghyun Moon, Geonho Nah, Donghyun Koh, Youngjun Cho, Johyun Park, Hojin Yoo, Jiho Park, Haneul Choi, Sungbin Moon, Taehoon Hwang, Seungwon Kim, Jaeyeong Kim, Seongjun Kim, Juneau Jung</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Recent advances in LLMs have made automated scientific research the next frontline in the path to artificial superintelligence. However, these systems are bound either to tasks of narrow scope or the limited creative capabilities of LLMs. We propose Spacer, a scientific discovery system that develops creative and factually grounded concepts without external intervention. Spacer attempts to achieve this via 'deliberate decontextualization,' an approach that disassembles information into atomic units - keywords - and draws creativity from unexplored connections between them. Spacer consists of (i) Nuri, an inspiration engine that builds keyword sets, and (ii) the Manifesting Pipeline that refines these sets into elaborate scientific statements. Nuri extracts novel, high-potential keyword sets from a keyword graph built with 180,000 academic publications in biological fields. The Manifesting Pipeline finds links between keywords, analyzes their logical structure, validates their plausibility, and ultimately drafts original scientific concepts. According to our experiments, the evaluation metric of Nuri accurately classifies high-impact publications with an AUROC score of 0.737. Our Manifesting Pipeline also successfully reconstructs core concepts from the latest top-journal articles solely from their keyword sets. An LLM-based scoring system estimates that this reconstruction was sound for over 85% of the cases. Finally, our embedding space analysis shows that outputs from Spacer are significantly more similar to leading publications compared with those from SOTA LLMs.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17661</guid>
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<pubDate>Mon, 25 Aug 2025 04:49:16 +0000</pubDate>
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<title>MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting</title>
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<link>https://arxiv.org/abs/2508.17811</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17811.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hanzhi Chang, Ruijie Zhu, Wenjie Chang, Mulin Yu, Yanzhe Liang, Jiahao Lu, Zhuoyuan Li, Tianzhu Zhang</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks. Project Page: https://hanzhichang.github.io/meshsplat_web</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17811</guid>
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<pubDate>Mon, 25 Aug 2025 09:04:20 +0000</pubDate>
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<title>Limitations of Normalization in Attention Mechanism</title>
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<link>https://arxiv.org/abs/2508.17821</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17821.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Timur Mudarisov, Mikhail Burtsev, Tatiana Petrova, Radu State</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model's ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17821</guid>
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<pubDate>Mon, 25 Aug 2025 09:25:05 +0000</pubDate>
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<title>Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation</title>
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<link>https://arxiv.org/abs/2508.17924</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17924.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Konstantin Egorov, Stepan Botman, Pavel Blinov, Galina Zubkova, Anton Ivaschenko, Alexander Kolsanov, Andrey Savchenko</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Progress in remote PhotoPlethysmoGraphy (rPPG) is limited by the critical issues of existing publicly available datasets: small size, privacy concerns with facial videos, and lack of diversity in conditions. The paper introduces a novel comprehensive large-scale multi-view video dataset for rPPG and health biomarkers estimation. Our dataset comprises 3600 synchronized video recordings from 600 subjects, captured under varied conditions (resting and post-exercise) using multiple consumer-grade cameras at different angles. To enable multimodal analysis of physiological states, each recording is paired with a 100 Hz PPG signal and extended health metrics, such as electrocardiogram, arterial blood pressure, biomarkers, temperature, oxygen saturation, respiratory rate, and stress level. Using this data, we train an efficient rPPG model and compare its quality with existing approaches in cross-dataset scenarios. The public release of our dataset and model should significantly speed up the progress in the development of AI medical assistants.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17924</guid>
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<pubDate>Mon, 25 Aug 2025 11:46:40 +0000</pubDate>
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<title>German4All - A Dataset and Model for Readability-Controlled Paraphrasing in German</title>
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<link>https://arxiv.org/abs/2508.17973</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.17973.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Miriam Anschütz, Thanh Mai Pham, Eslam Nasrallah, Maximilian Müller, Cristian-George Craciun, Georg Groh</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce German4All, the first large-scale German dataset of aligned readability-controlled, paragraph-level paraphrases. It spans five readability levels and comprises over 25,000 samples. The dataset is automatically synthesized using GPT-4 and rigorously evaluated through both human and LLM-based judgments. Using German4All, we train an open-source, readability-controlled paraphrasing model that achieves state-of-the-art performance in German text simplification, enabling more nuanced and reader-specific adaptations. We opensource both the dataset and the model to encourage further research on multi-level paraphrasing</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.17973</guid>
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<pubDate>Mon, 25 Aug 2025 12:40:32 +0000</pubDate>
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<title>Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation</title>
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<link>https://arxiv.org/abs/2508.18032</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18032.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yaqi Li, Peng Chen, Mingyang Han, Bu Pi, Haoxiang Shi, Runzhou Zhao, Yang Yao, Xuan Zhang, Jun Song</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> Despite the promising progress of recent autoregressive models in text-to-image (T2I) generation, their ability to handle multi-attribute and ambiguous prompts remains limited. To address these limitations, existing works have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and employed reinforcement learning (RL) to improve reasoning capabilities. However, most models provide reward signals only at the end of the generation stage. This monolithic final-only guidance makes it difficult to identify which stages contribute positively to the final outcome and may lead to suboptimal policies. To tackle this issue, we propose a Visual-Chain of Guidance (Visual-CoG) paradigm consisting of three stages: semantic reasoning, process refining, and outcome evaluation, with stage-aware rewards providing immediate guidance throughout the image generation pipeline. We further construct a visual cognition benchmark, VisCog-Bench, which comprises four subtasks to evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%, 5%, and 19%, respectively, demonstrating the superior performance of the proposed Visual-CoG. We will release all the resources soon.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18032</guid>
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<pubDate>Mon, 25 Aug 2025 13:53:02 +0000</pubDate>
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<title>Neither Valid nor Reliable? Investigating the Use of LLMs as Judges</title>
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<link>https://arxiv.org/abs/2508.18076</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18076.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Khaoula Chehbouni, Mohammed Haddou, Jackie Chi Kit Cheung, Golnoosh Farnadi</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs) that aims to be general-purpose. Recently, large language models as judges (LLJs) have emerged as a promising alternative to traditional metrics, but their validity remains underexplored. This position paper argues that the current enthusiasm around LLJs may be premature, as their adoption has outpaced rigorous scrutiny of their reliability and validity as evaluators. Drawing on measurement theory from the social sciences, we identify and critically assess four core assumptions underlying the use of LLJs: their ability to act as proxies for human judgment, their capabilities as evaluators, their scalability, and their cost-effectiveness. We examine how each of these assumptions may be challenged by the inherent limitations of LLMs, LLJs, or current practices in NLG evaluation. To ground our analysis, we explore three applications of LLJs: text summarization, data annotation, and safety alignment. Finally, we highlight the need for more responsible evaluation practices in LLJs evaluation, to ensure that their growing role in the field supports, rather than undermines, progress in NLG.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18076</guid>
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<pubDate>Mon, 25 Aug 2025 14:43:10 +0000</pubDate>
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<title>CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics</title>
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<link>https://arxiv.org/abs/2508.18124</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18124.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Weida Wang, Dongchen Huang, Jiatong Li, Tengchao Yang, Ziyang Zheng, Di Zhang, Dong Han, Benteng Chen, Binzhao Luo, Zhiyu Liu, Kunling Liu, Zhiyuan Gao, Shiqi Geng, Wei Ma, Jiaming Su, Xin Li, Shuchen Pu, Yuhan Shui, Qianjia Cheng, Zhihao Dou, Dongfei Cui, Changyong He, Jin Zeng, Zeke Xie, Mao Su, Dongzhan Zhou, Yuqiang Li, Wanli Ouyang, Yunqi Cai, Xi Dai, Shufei Zhang, Lei Bai, Jinguang Cheng, Zhong Fang, Hongming Weng</p><p><b>Upvotes:</b> 46</p><p><b>Summary:</b> We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than 520 graduate-level meticulously curated questions covering both representative subfields and foundational theoretical frameworks of condensed matter physics, such as magnetism, superconductivity, strongly correlated systems, etc. To ensure a deep understanding of the problem-solving process,we focus exclusively on calculation problems, requiring LLMs to independently generate comprehensive solutions. Meanwhile, leveraging tree-based representations of expressions, we introduce the Scalable Expression Edit Distance (SEED) score, which provides fine-grained (non-binary) partial credit and yields a more accurate assessment of similarity between prediction and ground-truth. Our results show that even the best models, Grok-4, reach only 36 average SEED score and 28% accuracy on CMPhysBench, underscoring a significant capability gap, especially for this practical and frontier domain relative to traditional physics. The code anddataset are publicly available at https://github.com/CMPhysBench/CMPhysBench.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18124</guid>
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<pubDate>Mon, 25 Aug 2025 15:32:22 +0000</pubDate>
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<title>SpotEdit: Evaluating Visually-Guided Image Editing Methods</title>
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<link>https://arxiv.org/abs/2508.18159</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18159.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sara Ghazanfari, Wei-An Lin, Haitong Tian, Ersin Yumer</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models have shown remarkable capabilities, existing evaluations remain simple and insufficiently representative of real-world editing challenges. We present SpotEdit, a comprehensive benchmark designed to systematically assess visually-guided image editing methods across diverse diffusion, autoregressive, and hybrid generative models, uncovering substantial performance disparities. To address a critical yet underexplored challenge, our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task. Our code and benchmark are publicly released at https://github.com/SaraGhazanfari/SpotEdit.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18159</guid>
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<pubDate>Mon, 25 Aug 2025 16:08:57 +0000</pubDate>
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<title>SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models</title>
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<link>https://arxiv.org/abs/2508.18179</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18179.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhenwei Tang, Difan Jiao, Blair Yang, Ashton Anderson</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences and asymmetric information. We introduce SEAM, a benchmark that pairs semantically equivalent inputs across four domains that have existing standardized textual and visual notations. By employing distinct notation systems across modalities, in contrast to OCR-based image-text pairing, SEAM provides a rigorous comparative assessment of the textual-symbolic and visual-spatial reasoning capabilities of VLMs. Across 21 contemporary models, we observe systematic modality imbalance: vision frequently lags language in overall performance, despite the problems containing semantically equivalent information, and cross-modal agreement is relatively low. Our error analysis reveals two main drivers: textual perception failures from tokenization in domain notation and visual perception failures that induce hallucinations. We also show that our results are largely robust to visual transformations. SEAM establishes a controlled, semantically equivalent setting for measuring and improving modality-agnostic reasoning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18179</guid>
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<pubDate>Mon, 25 Aug 2025 16:33:07 +0000</pubDate>
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<title>ST-Raptor: LLM-Powered Semi-Structured Table Question Answering</title>
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<link>https://arxiv.org/abs/2508.18190</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18190.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zirui Tang, Boyu Niu, Xuanhe Zhou, Boxiu Li, Wei Zhou, Jiannan Wang, Guoliang Li, Xinyi Zhang, Fan Wu</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Semi-structured tables, widely used in real-world applications (e.g., financial reports, medical records, transactional orders), often involve flexible and complex layouts (e.g., hierarchical headers and merged cells). These tables generally rely on human analysts to interpret table layouts and answer relevant natural language questions, which is costly and inefficient. To automate the procedure, existing methods face significant challenges. First, methods like NL2SQL require converting semi-structured tables into structured ones, which often causes substantial information loss. Second, methods like NL2Code and multi-modal LLM QA struggle to understand the complex layouts of semi-structured tables and cannot accurately answer corresponding questions. To this end, we propose ST-Raptor, a tree-based framework for semi-structured table question answering using large language models. First, we introduce the Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures complex semi-structured table layouts, along with an effective algorithm for constructing the tree. Second, we define a set of basic tree operations to guide LLMs in executing common QA tasks. Given a user question, ST-Raptor decomposes it into simpler sub-questions, generates corresponding tree operation pipelines, and conducts operation-table alignment for accurate pipeline execution. Third, we incorporate a two-stage verification mechanism: forward validation checks the correctness of execution steps, while backward validation evaluates answer reliability by reconstructing queries from predicted answers. To benchmark the performance, we present SSTQA, a dataset of 764 questions over 102 real-world semi-structured tables. Experiments show that ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code is available at https://github.com/weAIDB/ST-Raptor.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18190</guid>
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<pubDate>Mon, 25 Aug 2025 16:48:51 +0000</pubDate>
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<title>Unraveling the cognitive patterns of Large Language Models through module communities</title>
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<link>https://arxiv.org/abs/2508.18192</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18192.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and medical diagnostics to Chatbots. Despite their ubiquity and utility, the underlying mechanisms of LLM remain concealed within billions of parameters and complex structures, making their inner architecture and cognitive processes challenging to comprehend. We address this gap by adopting approaches to understanding emerging cognition in biology and developing a network-based framework that links cognitive skills, LLM architectures, and datasets, ushering in a paradigm shift in foundation model analysis. The skill distribution in the module communities demonstrates that while LLMs do not strictly parallel the focalized specialization observed in specific biological systems, they exhibit unique communities of modules whose emergent skill patterns partially mirror the distributed yet interconnected cognitive organization seen in avian and small mammalian brains. Our numerical results highlight a key divergence from biological systems to LLMs, where skill acquisition benefits substantially from dynamic, cross-regional interactions and neural plasticity. By integrating cognitive science principles with machine learning, our framework provides new insights into LLM interpretability and suggests that effective fine-tuning strategies should leverage distributed learning dynamics rather than rigid modular interventions.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18192</guid>
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<pubDate>Mon, 25 Aug 2025 16:49:38 +0000</pubDate>
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<title>Hermes 4 Technical Report</title>
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<link>https://arxiv.org/abs/2508.18255</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18255.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ryan Teknium, Roger Jin, Jai Suphavadeeprasit, Dakota Mahan, Jeffrey Quesnelle, Joe Li, Chen Guang, Shannon Sands, Karan Malhotra</p><p><b>Upvotes:</b> 30</p><p><b>Summary:</b> We present Hermes 4, a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. We describe the challenges encountered during data curation, synthesis, training, and evaluation, and outline the solutions employed to address these challenges at scale. We comprehensively evaluate across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks, and we report both quantitative performance and qualitative behavioral analysis. To support open research, all model weights are published publicly at https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18255</guid>
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<pubDate>Mon, 25 Aug 2025 17:45:06 +0000</pubDate>
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<title>MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs</title>
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<link>https://arxiv.org/abs/2508.18264</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18264.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sixun Dong, Juhua Hu, Mian Zhang, Ming Yin, Yanjie Fu, Qi Qian</p><p><b>Upvotes:</b> 26</p><p><b>Summary:</b> Vision-Language Models (VLMs) demonstrate impressive performance in understanding visual content with language instruction by converting visual input to vision tokens. However, redundancy in vision tokens results in the degenerated inference efficiency of VLMs. While many algorithms have been proposed to reduce the number of vision tokens, most of them apply only unimodal information (i.e., vision/text) for pruning and ignore the inherent multimodal property of vision-language tasks. Moreover, it lacks a generic criterion that can be applied to different modalities. To mitigate this limitation, in this work, we propose to leverage both vision and text tokens to select informative vision tokens by the criterion of coverage. We first formulate the subset selection problem as a maximum coverage problem. Afterward, a subset of vision tokens is optimized to cover the text tokens and the original set of vision tokens, simultaneously. Finally, a VLM agent can be adopted to further improve the quality of text tokens for guiding vision pruning. The proposed method MMTok is extensively evaluated on benchmark datasets with different VLMs. The comparison illustrates that vision and text information are complementary, and combining multimodal information can surpass the unimodal baseline with a clear margin. Moreover, under the maximum coverage criterion on the POPE dataset, our method achieves a 1.87x speedup while maintaining 98.7% of the original performance on LLaVA-NeXT-13B. Furthermore, with only four vision tokens, it still preserves 87.7% of the original performance on LLaVA-1.5-7B. These results highlight the effectiveness of coverage in token selection.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18264</guid>
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<pubDate>Mon, 25 Aug 2025 17:57:49 +0000</pubDate>
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<title>InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency</title>
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<link>https://arxiv.org/abs/2508.18265</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18265.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, Zhaokai Wang, Zhe Chen, Hongjie Zhang, Ganlin Yang, Haomin Wang, Qi Wei, Jinhui Yin, Wenhao Li, Erfei Cui, Guanzhou Chen, Zichen Ding, Changyao Tian, Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang, Yuchen Duan, Xuehui Wang, Songze Li, Xiangyu Zhao, Haodong Duan, Nianchen Deng, Bin Fu, Yinan He, Yi Wang, Conghui He, Botian Shi, Junjun He, Yingtong Xiong, Han Lv, Lijun Wu, Wenqi Shao, Kaipeng Zhang, Huipeng Deng, Biqing Qi, Jiaye Ge, Qipeng Guo, Wenwei Zhang, Wanli Ouyang, Limin Wang, Min Dou, Xizhou Zhu, Tong Lu, Dahua Lin, Jifeng Dai, Bowen Zhou, Weijie Su, Kai Chen, Yu Qiao, Wenhai Wang, Gen Luo</p><p><b>Upvotes:</b> 165</p><p><b>Summary:</b> We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning performance and a 4.05times inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks -- narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18265</guid>
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<pubDate>Mon, 25 Aug 2025 17:58:17 +0000</pubDate>
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<title>ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models</title>
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<link>https://arxiv.org/abs/2508.18271</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18271.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haitang Feng, Jie Liu, Jie Tang, Gangshan Wu, Beiqi Chen, Jianhuang Lai, Guangcong Wang</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> 3D inpainting often relies on multi-view 2D image inpainting, where the inherent inconsistencies across different inpainted views can result in blurred textures, spatial discontinuities, and distracting visual artifacts. These inconsistencies pose significant challenges when striving for accurate and realistic 3D object completion, particularly in applications that demand high fidelity and structural coherence. To overcome these limitations, we propose ObjFiller-3D, a novel method designed for the completion and editing of high-quality and consistent 3D objects. Instead of employing a conventional 2D image inpainting model, our approach leverages a curated selection of state-of-the-art video editing model to fill in the masked regions of 3D objects. We analyze the representation gap between 3D and videos, and propose an adaptation of a video inpainting model for 3D scene inpainting. In addition, we introduce a reference-based 3D inpainting method to further enhance the quality of reconstruction. Experiments across diverse datasets show that compared to previous methods, ObjFiller-3D produces more faithful and fine-grained reconstructions (PSNR of 26.6 vs. NeRFiller (15.9) and LPIPS of 0.19 vs. Instant3dit (0.25)). Moreover, it demonstrates strong potential for practical deployment in real-world 3D editing applications. Project page: https://objfiller3d.github.io/ Code: https://github.com/objfiller3d/ObjFiller-3D .</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18271</guid>
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<pubDate>Mon, 25 Aug 2025 17:59:40 +0000</pubDate>
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<title>Training Language Model Agents to Find Vulnerabilities with CTF-Dojo</title>
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<link>https://arxiv.org/abs/2508.18370</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18370.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Terry Yue Zhuo, Dingmin Wang, Hantian Ding, Varun Kumar, Zijian Wang</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Large language models (LLMs) have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting progress in training more capable ML agents. We introduce CTF-Dojo, the first large-scale executable runtime tailored for training LLMs with verifiable feedback, featuring 658 fully functional Capture-The-Flag (CTF)-style challenges containerized in Docker with guaranteed reproducibility. To enable rapid scaling without manual intervention, we develop CTF-Forge, an automated pipeline that transforms publicly available artifacts into ready-to-use execution environments in minutes, eliminating weeks of expert configuration traditionally required. We trained LLM-based agents on just 486 high-quality, execution-verified trajectories from CTF-Dojo, achieving up to 11.6% absolute gains over strong baselines across three competitive benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best-performing 32B model reaches 31.9% Pass@1, establishing a new open-weight state-of-the-art that rivals frontier models like DeepSeek-V3-0324 and Gemini-2.5-Flash. By framing CTF-style tasks as a benchmark for executable-agent learning, CTF-Dojo demonstrates that execution-grounded training signals are not only effective but pivotal in advancing high-performance ML agents without dependence on costly proprietary systems.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18370</guid>
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<pubDate>Mon, 25 Aug 2025 18:02:23 +0000</pubDate>
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<title>DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model</title>
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<link>https://arxiv.org/abs/2508.18579</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18579.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mohammadreza Ghaffarzadeh-Esfahani, Ali Motahharynia, Nahid Yousefian, Navid Mazrouei, Jafar Ghaisari, Yousof Gheisari</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Drug discovery is a complex and resource-intensive process, making early prediction of approval outcomes critical for optimizing research investments. While classical machine learning and deep learning methods have shown promise in drug approval prediction, their limited interpretability constraints their impact. Here, we present DrugReasoner, a reasoning-based large language model (LLM) built on the LLaMA architecture and fine-tuned with group relative policy optimization (GRPO) to predict the likelihood of small-molecule approval. DrugReasoner integrates molecular descriptors with comparative reasoning against structurally similar approved and unapproved compounds, generating predictions alongside step-by-step rationales and confidence scores. DrugReasoner achieved robust performance with an AUC of 0.732 and an F1 score of 0.729 on the validation set and 0.725 and 0.718 on the test set, respectively. These results outperformed conventional baselines, including logistic regression, support vector machine, and k-nearest neighbors and had competitive performance relative to XGBoost. On an external independent dataset, DrugReasoner outperformed both baseline and the recently developed ChemAP model, achieving an AUC of 0.728 and an F1-score of 0.774, while maintaining high precision and balanced sensitivity, demonstrating robustness in real-world scenarios. These findings demonstrate that DrugReasoner not only delivers competitive predictive accuracy but also enhances transparency through its reasoning outputs, thereby addressing a key bottleneck in AI-assisted drug discovery. This study highlights the potential of reasoning-augmented LLMs as interpretable and effective tools for pharmaceutical decision-making.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18579</guid>
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<pubDate>Tue, 26 Aug 2025 01:14:14 +0000</pubDate>
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<title>Wan-S2V: Audio-Driven Cinematic Video Generation</title>
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<link>https://arxiv.org/abs/2508.18621</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18621.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xin Gao, Li Hu, Siqi Hu, Mingyang Huang, Chaonan Ji, Dechao Meng, Jinwei Qi, Penchong Qiao, Zhen Shen, Yafei Song, Ke Sun, Linrui Tian, Guangyuan Wang, Qi Wang, Zhongjian Wang, Jiayu Xiao, Sheng Xu, Bang Zhang, Peng Zhang, Xindi Zhang, Zhe Zhang, Jingren Zhou, Lian Zhuo</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Current state-of-the-art (SOTA) methods for audio-driven character animation demonstrate promising performance for scenarios primarily involving speech and singing. However, they often fall short in more complex film and television productions, which demand sophisticated elements such as nuanced character interactions, realistic body movements, and dynamic camera work. To address this long-standing challenge of achieving film-level character animation, we propose an audio-driven model, which we refere to as Wan-S2V, built upon Wan. Our model achieves significantly enhanced expressiveness and fidelity in cinematic contexts compared to existing approaches. We conducted extensive experiments, benchmarking our method against cutting-edge models such as Hunyuan-Avatar and Omnihuman. The experimental results consistently demonstrate that our approach significantly outperforms these existing solutions. Additionally, we explore the versatility of our method through its applications in long-form video generation and precise video lip-sync editing.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18621</guid>
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<pubDate>Tue, 26 Aug 2025 02:51:31 +0000</pubDate>
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<title>ROSE: Remove Objects with Side Effects in Videos</title>
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<link>https://arxiv.org/abs/2508.18633</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18633.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chenxuan Miao, Yutong Feng, Jianshu Zeng, Zixiang Gao, Hantang Liu, Yunfeng Yan, Donglian Qi, Xi Chen, Bin Wang, Hengshuang Zhao</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Video object removal has achieved advanced performance due to the recent success of video generative models. However, when addressing the side effects of objects, e.g., their shadows and reflections, existing works struggle to eliminate these effects for the scarcity of paired video data as supervision. This paper presents ROSE, termed Remove Objects with Side Effects, a framework that systematically studies the object's effects on environment, which can be categorized into five common cases: shadows, reflections, light, translucency and mirror. Given the challenges of curating paired videos exhibiting the aforementioned effects, we leverage a 3D rendering engine for synthetic data generation. We carefully construct a fully-automatic pipeline for data preparation, which simulates a large-scale paired dataset with diverse scenes, objects, shooting angles, and camera trajectories. ROSE is implemented as an video inpainting model built on diffusion transformer. To localize all object-correlated areas, the entire video is fed into the model for reference-based erasing. Moreover, additional supervision is introduced to explicitly predict the areas affected by side effects, which can be revealed through the differential mask between the paired videos. To fully investigate the model performance on various side effect removal, we presents a new benchmark, dubbed ROSE-Bench, incorporating both common scenarios and the five special side effects for comprehensive evaluation. Experimental results demonstrate that ROSE achieves superior performance compared to existing video object erasing models and generalizes well to real-world video scenarios. The project page is https://rose2025-inpaint.github.io/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18633</guid>
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<pubDate>Tue, 26 Aug 2025 03:18:31 +0000</pubDate>
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<title>Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks</title>
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<link>https://arxiv.org/abs/2508.18672</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18672.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Taishi Nakamura, Satoki Ishikawa, Masaki Kawamura, Takumi Okamoto, Daisuke Nohara, Jun Suzuki, Rio Yokota</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Empirical scaling laws have driven the evolution of large language models (LLMs), yet their coefficients shift whenever the model architecture or data pipeline changes. Mixture-of-Experts (MoE) models, now standard in state-of-the-art systems, introduce a new sparsity dimension that current dense-model frontiers overlook. We investigate how MoE sparsity influences two distinct capability regimes: memorization and reasoning. We train families of MoE Transformers that systematically vary total parameters, active parameters, and top-k routing while holding the compute budget fixed. For every model we record pre-training loss, downstream task loss, and task accuracy, allowing us to separate the train-test generalization gap from the loss-accuracy gap. Memorization benchmarks improve monotonically with total parameters, mirroring training loss. By contrast, reasoning performance saturates and can even regress despite continued gains in both total parameters and training loss. Altering top-k alone has little effect when active parameters are constant, and classic hyperparameters such as learning rate and initialization modulate the generalization gap in the same direction as sparsity. Neither post-training reinforcement learning (GRPO) nor extra test-time compute rescues the reasoning deficit of overly sparse models. Our model checkpoints, code and logs are open-source at https://github.com/rioyokotalab/optimal-sparsity.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18672</guid>
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<pubDate>Tue, 26 Aug 2025 04:31:28 +0000</pubDate>
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<title>UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning</title>
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<link>https://arxiv.org/abs/2508.18756</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18756.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zihao Huang, Yu Bao, Qiyang Min, Siyan Chen, Ran Guo, Hongzhi Huang, Defa Zhu, Yutao Zeng, Banggu Wu, Xun Zhou, Siyuan Qiao</p><p><b>Upvotes:</b> 32</p><p><b>Summary:</b> While Mixture of Experts (MoE) models achieve remarkable efficiency by activating only subsets of parameters, they suffer from high memory access costs during inference. Memory-layer architectures offer an appealing alternative with very few memory access, but previous attempts like UltraMem have only matched the performance of 2-expert MoE models, falling significantly short of state-of-the-art 8-expert configurations. We present UltraMemV2, a redesigned memory-layer architecture that closes this performance gap. Our approach introduces five key improvements: integrating memory layers into every transformer block, simplifying value expansion with single linear projections, adopting FFN-based value processing from PEER, implementing principled parameter initialization, and rebalancing memory-to-FFN computation ratios. Through extensive evaluation, we demonstrate that UltraMemV2 achieves performance parity with 8-expert MoE models under same computation and parameters but significantly low memory access. Notably, UltraMemV2 shows superior performance on memory-intensive tasks, with improvements of +1.6 points on long-context memorization, +6.2 points on multi-round memorization, and +7.9 points on in-context learning. We validate our approach at scale with models up to 2.5B activated parameters from 120B total parameters, and establish that activation density has greater impact on performance than total sparse parameter count. Our work brings memory-layer architectures to performance parity with state-of-the-art MoE models, presenting a compelling alternative for efficient sparse computation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18756</guid>
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<pubDate>Tue, 26 Aug 2025 07:33:11 +0000</pubDate>
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<title>ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models</title>
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<link>https://arxiv.org/abs/2508.18773</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18773.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qianyu He, Siyu Yuan, Xuefeng Li, Mingxuan Wang, Jiangjie Chen</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Large language models (LLMs) with chain-of-thought reasoning have demonstrated remarkable problem-solving capabilities, but controlling their computational effort remains a significant challenge for practical deployment. Recent proprietary systems like OpenAI's gpt-oss series have introduced discrete operational modes for intuitive reasoning control, but the open-source community has largely failed to achieve such capabilities. In this paper, we introduce ThinkDial, the first open-recipe end-to-end framework that successfully implements gpt-oss-style controllable reasoning through discrete operational modes. Our system enables seamless switching between three distinct reasoning regimes: High mode (full reasoning capability), Medium mode (50 percent token reduction with <10 percent performance degradation), and Low mode (75 percent token reduction with <15 percent performance degradation). We achieve this through an end-to-end training paradigm that integrates budget-mode control throughout the entire pipeline: budget-mode supervised fine-tuning that embeds controllable reasoning capabilities directly into the learning process, and two-phase budget-aware reinforcement learning with adaptive reward shaping. Extensive experiments demonstrate that ThinkDial achieves target compression-performance trade-offs with clear response length reductions while maintaining performance thresholds. The framework also exhibits strong generalization capabilities on out-of-distribution tasks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18773</guid>
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<pubDate>Tue, 26 Aug 2025 07:57:28 +0000</pubDate>
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<title>Forecasting Probability Distributions of Financial Returns with Deep Neural Networks</title>
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<link>https://arxiv.org/abs/2508.18921</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18921.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jakub Michańków</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> This study evaluates deep neural networks for forecasting probability distributions of financial returns. 1D convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) architectures are used to forecast parameters of three probability distributions: Normal, Student's t, and skewed Student's t. Using custom negative log-likelihood loss functions, distribution parameters are optimized directly. The models are tested on six major equity indices (S\&P 500, BOVESPA, DAX, WIG, Nikkei 225, and KOSPI) using probabilistic evaluation metrics including Log Predictive Score (LPS), Continuous Ranked Probability Score (CRPS), and Probability Integral Transform (PIT). Results show that deep learning models provide accurate distributional forecasts and perform competitively with classical GARCH models for Value-at-Risk estimation. The LSTM with skewed Student's t distribution performs best across multiple evaluation criteria, capturing both heavy tails and asymmetry in financial returns. This work shows that deep neural networks are viable alternatives to traditional econometric models for financial risk assessment and portfolio management.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18921</guid>
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<pubDate>Tue, 26 Aug 2025 10:48:16 +0000</pubDate>
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<title>USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning</title>
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<link>https://arxiv.org/abs/2508.18966</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.18966.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shaojin Wu, Mengqi Huang, Yufeng Cheng, Wenxu Wu, Jiahe Tian, Yiming Luo, Fei Ding, Qian He</p><p><b>Upvotes:</b> 45</p><p><b>Summary:</b> Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of content and style, a long-standing theme in style-driven research. To this end, we present USO, a Unified Style-Subject Optimized customization model. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content-style disentanglement training. Third, we incorporate a style reward-learning paradigm denoted as SRL to further enhance the model's performance. Finally, we release USO-Bench, the first benchmark that jointly evaluates style similarity and subject fidelity across multiple metrics. Extensive experiments demonstrate that USO achieves state-of-the-art performance among open-source models along both dimensions of subject consistency and style similarity. Code and model: https://github.com/bytedance/USO</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.18966</guid>
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<pubDate>Tue, 26 Aug 2025 12:10:24 +0000</pubDate>
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<title>MovieCORE: COgnitive REasoning in Movies</title>
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<link>https://arxiv.org/abs/2508.19026</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19026.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh, Ying Cheng, Hung-Ting Su, Yung-Hao Tang, Shang-Hong Lai, Winston H. Hsu</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> This paper introduces MovieCORE, a novel video question answering (VQA) dataset designed to probe deeper cognitive understanding of movie content. Unlike existing datasets that focus on surface-level comprehension, MovieCORE emphasizes questions that engage System-2 thinking while remaining specific to the video material. We present an innovative agentic brainstorming approach, utilizing multiple large language models (LLMs) as thought agents to generate and refine high-quality question-answer pairs. To evaluate dataset quality, we develop a set of cognitive tests assessing depth, thought-provocation potential, and syntactic complexity. We also propose a comprehensive evaluation scheme for assessing VQA model performance on deeper cognitive tasks. To address the limitations of existing video-language models (VLMs), we introduce an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves model reasoning capabilities post-training by up to 25%. Our work contributes to advancing movie understanding in AI systems and provides valuable insights into the capabilities and limitations of current VQA models when faced with more challenging, nuanced questions about cinematic content. Our project page, dataset and code can be found at https://joslefaure.github.io/assets/html/moviecore.html.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19026</guid>
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<pubDate>Tue, 26 Aug 2025 13:43:45 +0000</pubDate>
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<title>MIDAS: Multimodal Interactive Digital-human Synthesis via Real-time Autoregressive Video Generation</title>
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<link>https://arxiv.org/abs/2508.19320</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19320.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ming Chen, Liyuan Cui, Wenyuan Zhang, Haoxian Zhang, Yan Zhou, Xiaohan Li, Xiaoqiang Liu, Pengfei Wan</p><p><b>Upvotes:</b> 26</p><p><b>Summary:</b> Recently, interactive digital human video generation has attracted widespread attention and achieved remarkable progress. However, building such a practical system that can interact with diverse input signals in real time remains challenging to existing methods, which often struggle with high latency, heavy computational cost, and limited controllability. In this work, we introduce an autoregressive video generation framework that enables interactive multimodal control and low-latency extrapolation in a streaming manner. With minimal modifications to a standard large language model (LLM), our framework accepts multimodal condition encodings including audio, pose, and text, and outputs spatially and semantically coherent representations to guide the denoising process of a diffusion head. To support this, we construct a large-scale dialogue dataset of approximately 20,000 hours from multiple sources, providing rich conversational scenarios for training. We further introduce a deep compression autoencoder with up to 64times reduction ratio, which effectively alleviates the long-horizon inference burden of the autoregressive model. Extensive experiments on duplex conversation, multilingual human synthesis, and interactive world model highlight the advantages of our approach in low latency, high efficiency, and fine-grained multimodal controllability.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19320</guid>
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<pubDate>Tue, 26 Aug 2025 14:00:16 +0000</pubDate>
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<title>FastMesh:Efficient Artistic Mesh Generation via Component Decoupling</title>
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<link>https://arxiv.org/abs/2508.19188</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19188.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jeonghwan Kim, Yushi Lan, Armando Fortes, Yongwei Chen, Xingang Pan</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully represent manifold meshes, as each vertex is shared by multiple faces. This redundancy leads to excessively long token sequences and inefficient generation processes. In this paper, we propose an efficient framework that generates artistic meshes by treating vertices and faces separately, significantly reducing redundancy. We employ an autoregressive model solely for vertex generation, decreasing the token count to approximately 23\% of that required by the most compact existing tokenizer. Next, we leverage a bidirectional transformer to complete the mesh in a single step by capturing inter-vertex relationships and constructing the adjacency matrix that defines the mesh faces. To further improve the generation quality, we introduce a fidelity enhancer to refine vertex positioning into more natural arrangements and propose a post-processing framework to remove undesirable edge connections. Experimental results show that our method achieves more than 8times faster speed on mesh generation compared to state-of-the-art approaches, while producing higher mesh quality.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19188</guid>
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<pubDate>Tue, 26 Aug 2025 16:51:02 +0000</pubDate>
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<title>Understanding Tool-Integrated Reasoning</title>
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<link>https://arxiv.org/abs/2508.19201</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19201.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Heng Lin, Zhongwen Xu</p><p><b>Upvotes:</b> 28</p><p><b>Summary:</b> We study why Tool-Integrated Reasoning (TIR) makes Large Language Models (LLMs) more capable. While LLMs integrated with tools like Python code interpreters show great promise, a principled theory explaining why this paradigm is effective has been missing. This work provides the first formal proof that TIR fundamentally expands an LLM's capabilities. We demonstrate that tools enable a strict expansion of the model's empirical and feasible support, breaking the capability ceiling of pure-text models by unlocking problem-solving strategies that are otherwise impossible or intractably verbose. To guide model behavior without compromising training stability and performance, we also introduce Advantage Shaping Policy Optimization (ASPO), a novel algorithm that directly modifies the advantage function to guide the policy behavior. We conduct comprehensive experiments on challenging mathematical benchmarks, leveraging a Python interpreter as the external tool. Our results show that the TIR model decisively outperforms its pure-text counterpart on the pass@k metric. Crucially, this advantage is not confined to computationally-intensive problems but extends to those requiring significant abstract insight. We further identify the emergent cognitive patterns that illustrate how models learn to think with tools. Finally, we report improved tool usage behavior with early code invocation and much more interactive turns with ASPO. Overall, our work provides the first principled explanation for TIR's success, shifting the focus from the mere fact that tools work to why and how they enable more powerful reasoning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19201</guid>
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<pubDate>Tue, 26 Aug 2025 17:03:46 +0000</pubDate>
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<title>Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning</title>
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<link>https://arxiv.org/abs/2508.19202</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19202.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Alan Li, Yixin Liu, Arpan Sarkar, Doug Downey, Arman Cohan</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Scientific problem solving poses unique challenges for LLMs, requiring both deep domain knowledge and the ability to apply such knowledge through complex reasoning. While automated scientific reasoners hold great promise for assisting human scientists, there is currently no widely adopted holistic benchmark for evaluating scientific reasoning, and few approaches systematically disentangle the distinct roles of knowledge and reasoning in these tasks. To address these gaps, we introduce SciReas, a diverse suite of existing benchmarks for scientific reasoning tasks, and SciReas-Pro, a selective subset that requires more complex reasoning. Our holistic evaluation surfaces insights about scientific reasoning performance that remain hidden when relying on individual benchmarks alone. We then propose KRUX, a probing framework for studying the distinct roles of reasoning and knowledge in scientific tasks. Combining the two, we conduct an in-depth analysis that yields several key findings: (1) Retrieving task-relevant knowledge from model parameters is a critical bottleneck for LLMs in scientific reasoning; (2) Reasoning models consistently benefit from external knowledge added in-context on top of the reasoning enhancement; (3) Enhancing verbalized reasoning improves LLMs' ability to surface task-relevant knowledge. Finally, we conduct a lightweight analysis, comparing our science-focused data composition with concurrent efforts on long CoT SFT, and release SciLit01, a strong 8B baseline for scientific reasoning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19202</guid>
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<pubDate>Tue, 26 Aug 2025 17:04:23 +0000</pubDate>
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<title>VibeVoice Technical Report</title>
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<link>https://arxiv.org/abs/2508.19205</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19205.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhiliang Peng, Jianwei Yu, Wenhui Wang, Yaoyao Chang, Yutao Sun, Li Dong, Yi Zhu, Weijiang Xu, Hangbo Bao, Zehua Wang, Shaohan Huang, Yan Xia, Furu Wei</p><p><b>Upvotes:</b> 102</p><p><b>Summary:</b> This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion. To enable this, we introduce a novel continuous speech tokenizer that, when compared to the popular Encodec model, improves data compression by 80 times while maintaining comparable performance. The tokenizer effectively preserves audio fidelity while significantly boosting computational efficiency for processing long sequences. Thus, VibeVoice can synthesize long-form speech for up to 90 minutes (in a 64K context window length) with a maximum of 4 speakers, capturing the authentic conversational ``vibe'' and surpassing open-source and proprietary dialogue models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19205</guid>
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<pubDate>Tue, 26 Aug 2025 17:09:12 +0000</pubDate>
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<title>OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation</title>
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<link>https://arxiv.org/abs/2508.19209</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19209.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jianwen Jiang, Weihong Zeng, Zerong Zheng, Jiaqi Yang, Chao Liang, Wang Liao, Han Liang, Yuan Zhang, Mingyuan Gao</p><p><b>Upvotes:</b> 36</p><p><b>Summary:</b> Existing video avatar models can produce fluid human animations, yet they struggle to move beyond mere physical likeness to capture a character's authentic essence. Their motions typically synchronize with low-level cues like audio rhythm, lacking a deeper semantic understanding of emotion, intent, or context. To bridge this gap, we propose a framework designed to generate character animations that are not only physically plausible but also semantically coherent and expressive. Our model, OmniHuman-1.5, is built upon two key technical contributions. First, we leverage Multimodal Large Language Models to synthesize a structured textual representation of conditions that provides high-level semantic guidance. This guidance steers our motion generator beyond simplistic rhythmic synchronization, enabling the production of actions that are contextually and emotionally resonant. Second, to ensure the effective fusion of these multimodal inputs and mitigate inter-modality conflicts, we introduce a specialized Multimodal DiT architecture with a novel Pseudo Last Frame design. The synergy of these components allows our model to accurately interpret the joint semantics of audio, images, and text, thereby generating motions that are deeply coherent with the character, scene, and linguistic content. Extensive experiments demonstrate that our model achieves leading performance across a comprehensive set of metrics, including lip-sync accuracy, video quality, motion naturalness and semantic consistency with textual prompts. Furthermore, our approach shows remarkable extensibility to complex scenarios, such as those involving multi-person and non-human subjects. Homepage: https://omnihuman-lab.github.io/v1_5/</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19209</guid>
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<pubDate>Tue, 26 Aug 2025 17:15:26 +0000</pubDate>
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<title>Predicting the Order of Upcoming Tokens Improves Language Modeling</title>
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<link>https://arxiv.org/abs/2508.19228</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19228.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zayd M. K. Zuhri, Erland Hilman Fuadi, Alham Fikri Aji</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Multi-Token Prediction (MTP) has been proposed as an auxiliary objective to improve next-token prediction (NTP) in language model training but shows inconsistent improvements, underperforming in standard NLP benchmarks. We argue that MTP's exact future token prediction is too difficult as an auxiliary loss. Instead, we propose Token Order Prediction (TOP), which trains models to order upcoming tokens by their proximity using a learning-to-rank loss. TOP requires only a single additional unembedding layer compared to MTP's multiple transformer layers. We pretrain models of 340M, 1.8B, and 7B parameters using NTP, MTP, and TOP objectives. Results on eight standard NLP benchmarks show that TOP overall outperforms both NTP and MTP even at scale. Our code is available at https://github.com/zaydzuhri/token-order-prediction</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19228</guid>
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<pubDate>Tue, 26 Aug 2025 17:43:30 +0000</pubDate>
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<title>StepWiser: Stepwise Generative Judges for Wiser Reasoning</title>
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<link>https://arxiv.org/abs/2508.19229</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19229.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wei Xiong, Wenting Zhao, Weizhe Yuan, Olga Golovneva, Tong Zhang, Jason Weston, Sainbayar Sukhbaatar</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> As models increasingly leverage multi-step reasoning strategies to solve complex problems, supervising the logical validity of these intermediate steps has become a critical research challenge. Process reward models address this by providing step-by-step feedback, but current approaches have two major drawbacks: they typically function as classifiers without providing explanations, and their reliance on supervised fine-tuning with static datasets limits generalization. Inspired by recent advances, we reframe stepwise reward modeling from a classification task to a reasoning task itself. We thus propose a generative judge that reasons about the policy model's reasoning steps (i.e., meta-reasons), outputting thinking tokens before delivering a final verdict. Our model, StepWiser, is trained by reinforcement learning using relative outcomes of rollouts. We show it provides (i) better judgment accuracy on intermediate steps than existing methods; (ii) can be used to improve the policy model at training time; and (iii) improves inference-time search.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19229</guid>
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<pubDate>Tue, 26 Aug 2025 17:45:05 +0000</pubDate>
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<title>Autoregressive Universal Video Segmentation Model</title>
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<link>https://arxiv.org/abs/2508.19242</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19242.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Miran Heo, Sukjun Hwang, Min-Hung Chen, Yu-Chiang Frank Wang, Albert Gu, Seon Joo Kim, Ryo Hachiuma</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Recent video foundation models such as SAM2 excel at prompted video segmentation by treating masks as a general-purpose primitive. However, many real-world settings require unprompted segmentation that aims to detect and track all objects in a video without external cues, leaving today's landscape fragmented across task-specific models and pipelines. We recast streaming video segmentation as sequential mask prediction, analogous to language modeling, and introduce the Autoregressive Universal Segmentation Model (AUSM), a single architecture that unifies both prompted and unprompted video segmentation. Built on recent state-space models, AUSM maintains a fixed-size spatial state and scales to video streams of arbitrary length. Furthermore, all components of AUSM are designed for parallel training across frames, yielding substantial speedups over iterative training. On standard benchmarks (DAVIS17, YouTube-VOS 2018 & 2019, MOSE, YouTube-VIS 2019 & 2021, and OVIS) AUSM outperforms prior universal streaming video segmentation methods and achieves up to 2.5x faster training on 16-frame sequences.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19242</guid>
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<pubDate>Tue, 26 Aug 2025 17:59:13 +0000</pubDate>
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<title>VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space</title>
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<link>https://arxiv.org/abs/2508.19247</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19247.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Lin Li, Zehuan Huang, Haoran Feng, Gengxiong Zhuang, Rui Chen, Chunchao Guo, Lu Sheng</p><p><b>Upvotes:</b> 36</p><p><b>Summary:</b> 3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, but they face challenges in precisely preserving unedited regions and overall coherence. Inspired by structured 3D generative models, we propose VoxHammer, a novel training-free approach that performs precise and coherent editing in 3D latent space. Given a 3D model, VoxHammer first predicts its inversion trajectory and obtains its inverted latents and key-value tokens at each timestep. Subsequently, in the denoising and editing phase, we replace the denoising features of preserved regions with the corresponding inverted latents and cached key-value tokens. By retaining these contextual features, this approach ensures consistent reconstruction of preserved areas and coherent integration of edited parts. To evaluate the consistency of preserved regions, we constructed Edit3D-Bench, a human-annotated dataset comprising hundreds of samples, each with carefully labeled 3D editing regions. Experiments demonstrate that VoxHammer significantly outperforms existing methods in terms of both 3D consistency of preserved regions and overall quality. Our method holds promise for synthesizing high-quality edited paired data, thereby laying the data foundation for in-context 3D generation. See our project page at https://huanngzh.github.io/VoxHammer-Page/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19247</guid>
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<pubDate>Tue, 26 Aug 2025 17:59:47 +0000</pubDate>
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<title>Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents</title>
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<link>https://arxiv.org/abs/2508.19493</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19493.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhixin Lin, Jungang Li, Shidong Pan, Yibo Shi, Yue Yao, Dongliang Xu</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> Smartphones bring significant convenience to users but also enable devices to extensively record various types of personal information. Existing smartphone agents powered by Multimodal Large Language Models (MLLMs) have achieved remarkable performance in automating different tasks. However, as the cost, these agents are granted substantial access to sensitive users' personal information during this operation. To gain a thorough understanding of the privacy awareness of these agents, we present the first large-scale benchmark encompassing 7,138 scenarios to the best of our knowledge. In addition, for privacy context in scenarios, we annotate its type (e.g., Account Credentials), sensitivity level, and location. We then carefully benchmark seven available mainstream smartphone agents. Our results demonstrate that almost all benchmarked agents show unsatisfying privacy awareness (RA), with performance remaining below 60% even with explicit hints. Overall, closed-source agents show better privacy ability than open-source ones, and Gemini 2.0-flash achieves the best, achieving an RA of 67%. We also find that the agents' privacy detection capability is highly related to scenario sensitivity level, i.e., the scenario with a higher sensitivity level is typically more identifiable. We hope the findings enlighten the research community to rethink the unbalanced utility-privacy tradeoff about smartphone agents. Our code and benchmark are available at https://zhixin-l.github.io/SAPA-Bench.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19493</guid>
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<pubDate>Wed, 27 Aug 2025 00:41:28 +0000</pubDate>
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<title>Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills</title>
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<link>https://arxiv.org/abs/2508.19500</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19500.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> David Noever</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> This paper identifies and analyzes a novel vulnerability class in Model Context Protocol (MCP) based agent systems. The attack chain describes and demonstrates how benign, individually authorized tasks can be orchestrated to produce harmful emergent behaviors. Through systematic analysis using the MITRE ATLAS framework, we demonstrate how 95 agents tested with access to multiple services-including browser automation, financial analysis, location tracking, and code deployment-can chain legitimate operations into sophisticated attack sequences that extend beyond the security boundaries of any individual service. These red team exercises survey whether current MCP architectures lack cross-domain security measures necessary to detect or prevent a large category of compositional attacks. We present empirical evidence of specific attack chains that achieve targeted harm through service orchestration, including data exfiltration, financial manipulation, and infrastructure compromise. These findings reveal that the fundamental security assumption of service isolation fails when agents can coordinate actions across multiple domains, creating an exponential attack surface that grows with each additional capability. This research provides a barebones experimental framework that evaluate not whether agents can complete MCP benchmark tasks, but what happens when they complete them too well and optimize across multiple services in ways that violate human expectations and safety constraints. We propose three concrete experimental directions using the existing MCP benchmark suite.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19500</guid>
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<pubDate>Wed, 27 Aug 2025 01:11:59 +0000</pubDate>
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<title>MotionFlux: Efficient Text-Guided Motion Generation through Rectified Flow Matching and Preference Alignment</title>
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<link>https://arxiv.org/abs/2508.19527</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19527.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhiting Gao, Dan Song, Diqiong Jiang, Chao Xue, An-An Liu</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Motion generation is essential for animating virtual characters and embodied agents. While recent text-driven methods have made significant strides, they often struggle with achieving precise alignment between linguistic descriptions and motion semantics, as well as with the inefficiencies of slow, multi-step inference. To address these issues, we introduce TMR++ Aligned Preference Optimization (TAPO), an innovative framework that aligns subtle motion variations with textual modifiers and incorporates iterative adjustments to reinforce semantic grounding. To further enable real-time synthesis, we propose MotionFLUX, a high-speed generation framework based on deterministic rectified flow matching. Unlike traditional diffusion models, which require hundreds of denoising steps, MotionFLUX constructs optimal transport paths between noise distributions and motion spaces, facilitating real-time synthesis. The linearized probability paths reduce the need for multi-step sampling typical of sequential methods, significantly accelerating inference time without sacrificing motion quality. Experimental results demonstrate that, together, TAPO and MotionFLUX form a unified system that outperforms state-of-the-art approaches in both semantic consistency and motion quality, while also accelerating generation speed. The code and pretrained models will be released.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19527</guid>
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<pubDate>Wed, 27 Aug 2025 02:45:09 +0000</pubDate>
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<title>Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference</title>
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<link>https://arxiv.org/abs/2508.19559</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19559.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Rongzhi Li, Ruogu Du, Zefang Chu, Sida Zhao, Chunlei Han, Zuocheng Shi, Yiwen Shao, Huanle Han, Long Huang, Zherui Liu, Shufan Liu</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Serving Large Language Models (LLMs) is a GPU-intensive task where traditional autoscalers fall short, particularly for modern Prefill-Decode (P/D) disaggregated architectures. This architectural shift, while powerful, introduces significant operational challenges, including inefficient use of heterogeneous hardware, network bottlenecks, and critical imbalances between prefill and decode stages. We introduce HeteroScale, a coordinated autoscaling framework that addresses the core challenges of P/D disaggregated serving. HeteroScale combines a topology-aware scheduler that adapts to heterogeneous hardware and network constraints with a novel metric-driven policy derived from the first large-scale empirical study of autoscaling signals in production. By leveraging a single, robust metric to jointly scale prefill and decode pools, HeteroScale maintains architectural balance while ensuring efficient, adaptive resource management. Deployed in a massive production environment on tens of thousands of GPUs, HeteroScale has proven its effectiveness, increasing average GPU utilization by a significant 26.6 percentage points and saving hundreds of thousands of GPU-hours daily, all while upholding stringent service level objectives.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19559</guid>
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<pubDate>Wed, 27 Aug 2025 04:22:02 +0000</pubDate>
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<title>Self-Rewarding Vision-Language Model via Reasoning Decomposition</title>
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<link>https://arxiv.org/abs/2508.19652</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19652.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zongxia Li, Wenhao Yu, Chengsong Huang, Rui Liu, Zhenwen Liang, Fuxiao Liu, Jingxi Che, Dian Yu, Jordan Boyd-Graber, Haitao Mi, Dong Yu</p><p><b>Upvotes:</b> 77</p><p><b>Summary:</b> Vision-Language Models (VLMs) often suffer from visual hallucinations, saying things that are not actually in the image, and language shortcuts, where they skip the visual part and just rely on text priors. These issues arise because most post-training methods for VLMs rely on simple verifiable answer matching and supervise only final outputs, leaving intermediate visual reasoning without explicit guidance. As a result, VLMs receive sparse visual signals and often learn to prioritize language-based reasoning over visual perception. To mitigate this, some existing methods add visual supervision using human annotations or distilled labels from external large models. However, human annotations are labor-intensive and costly, and because external signals cannot adapt to the evolving policy, they cause distributional shifts that can lead to reward hacking. In this paper, we introduce Vision-SR1, a self-rewarding method that improves visual reasoning without relying on external visual supervisions via reinforcement learning. Vision-SR1 decomposes VLM reasoning into two stages: visual perception and language reasoning. The model is first prompted to produce self-contained visual perceptions that are sufficient to answer the question without referring back the input image. To validate this self-containment, the same VLM model is then re-prompted to perform language reasoning using only the generated perception as input to compute reward. This self-reward is combined with supervision on final outputs, providing a balanced training signal that strengthens both visual perception and language reasoning. Our experiments demonstrate that Vision-SR1 improves visual reasoning, mitigates visual hallucinations, and reduces reliance on language shortcuts across diverse vision-language tasks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19652</guid>
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<pubDate>Wed, 27 Aug 2025 08:01:03 +0000</pubDate>
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<title>Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?</title>
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<link>https://arxiv.org/abs/2508.19827</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19827.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao, Nikolaos Aletras</p><p><b>Upvotes:</b> 25</p><p><b>Summary:</b> Recent work has demonstrated that Chain-of-Thought (CoT) often yields limited gains for soft-reasoning problems such as analytical and commonsense reasoning. CoT can also be unfaithful to a model's actual reasoning. We investigate the dynamics and faithfulness of CoT in soft-reasoning tasks across instruction-tuned, reasoning and reasoning-distilled models. Our findings reveal differences in how these models rely on CoT, and show that CoT influence and faithfulness are not always aligned.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19827</guid>
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<pubDate>Wed, 27 Aug 2025 12:25:29 +0000</pubDate>
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<title>Diffusion Language Models Know the Answer Before Decoding</title>
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<link>https://arxiv.org/abs/2508.19982</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19982.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Li Shen, Yi Liang, Soroush Vosoughi, Shiwei Liu</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and flexible token orders. However, their inference remains slower than that of autoregressive models, primarily due to the cost of bidirectional attention and the large number of refinement steps required for high quality outputs. In this work, we highlight and leverage an overlooked property of DLMs early answer convergence: in many cases, the correct answer can be internally identified by half steps before the final decoding step, both under semi-autoregressive and random remasking schedules. For example, on GSM8K and MMLU, up to 97% and 99% of instances, respectively, can be decoded correctly using only half of the refinement steps. Building on this observation, we introduce Prophet, a training-free fast decoding paradigm that enables early commit decoding. Specifically, Prophet dynamically decides whether to continue refinement or to go "all-in" (i.e., decode all remaining tokens in one step), using the confidence gap between the top-2 prediction candidates as the criterion. It integrates seamlessly into existing DLM implementations, incurs negligible overhead, and requires no additional training. Empirical evaluations of LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3.4x while preserving high generation quality. These results recast DLM decoding as a problem of when to stop sampling, and demonstrate that early decode convergence provides a simple yet powerful mechanism for accelerating DLM inference, complementary to existing speedup techniques. Our code is publicly available at https://github.com/pixeli99/Prophet.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.19982</guid>
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<pubDate>Wed, 27 Aug 2025 15:40:25 +0000</pubDate>
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<title>DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis</title>
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<link>https://arxiv.org/abs/2508.20033</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20033.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Liana Patel, Negar Arabzadeh, Harshit Gupta, Ankita Sundar, Ion Stoica, Matei Zaharia, Carlos Guestrin</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> The ability to research and synthesize knowledge is central to human expertise and progress. An emerging class of systems promises these exciting capabilities through generative research synthesis, performing retrieval over the live web and synthesizing discovered sources into long-form, cited summaries. However, evaluating such systems remains an open challenge: existing question-answering benchmarks focus on short-form factual responses, while expert-curated datasets risk staleness and data contamination. Both fail to capture the complexity and evolving nature of real research synthesis tasks. In this work, we introduce DeepScholar-bench, a live benchmark and holistic, automated evaluation framework designed to evaluate generative research synthesis. DeepScholar-bench draws queries from recent, high-quality ArXiv papers and focuses on a real research synthesis task: generating the related work sections of a paper by retrieving, synthesizing, and citing prior research. Our evaluation framework holistically assesses performance across three key dimensions, knowledge synthesis, retrieval quality, and verifiability. We also develop DeepScholar-base, a reference pipeline implemented efficiently using the LOTUS API. Using the DeepScholar-bench framework, we perform a systematic evaluation of prior open-source systems, search AI's, OpenAI's DeepResearch, and DeepScholar-base. We find that DeepScholar-base establishes a strong baseline, attaining competitive or higher performance than each other method. We also find that DeepScholar-bench remains far from saturated, with no system exceeding a score of 19% across all metrics. These results underscore the difficulty of DeepScholar-bench, as well as its importance for progress towards AI systems capable of generative research synthesis. We make our code available at https://github.com/guestrin-lab/deepscholar-bench.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20033</guid>
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<pubDate>Wed, 27 Aug 2025 16:36:34 +0000</pubDate>
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<title>Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies</title>
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<link>https://arxiv.org/abs/2508.20072</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20072.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhixuan Liang, Yizhuo Li, Tianshuo Yang, Chengyue Wu, Sitong Mao, Liuao Pei, Xiaokang Yang, Jiangmiao Pang, Yao Mu, Ping Luo</p><p><b>Upvotes:</b> 26</p><p><b>Summary:</b> Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions to robot actions. However, prevailing VLA decoders either generate actions autoregressively in a fixed left-to-right order or attach continuous diffusion or flow matching heads outside the backbone, demanding specialized training and iterative sampling that hinder a unified, scalable architecture. We present Discrete Diffusion VLA, a single-transformer policy that models discretized action chunks with discrete diffusion and is trained with the same cross-entropy objective as the VLM backbone. The design retains diffusion's progressive refinement paradigm while remaining natively compatible with the discrete token interface of VLMs. Our method achieves an adaptive decoding order that resolves easy action elements before harder ones and uses secondary remasking to revisit uncertain predictions across refinement rounds, which improves consistency and enables robust error correction. This unified decoder preserves pretrained vision language priors, supports parallel decoding, breaks the autoregressive bottleneck, and reduces the number of function evaluations. Discrete Diffusion VLA achieves 96.3% avg. SR on LIBERO, 71.2% visual matching on SimplerEnv Fractal and 49.3% overall on SimplerEnv Bridge, improving over both autoregressive and continuous diffusion baselines. These findings indicate that discrete-diffusion action decoder supports precise action modeling and consistent training, laying groundwork for scaling VLA to larger models and datasets.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20072</guid>
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<pubDate>Wed, 27 Aug 2025 17:39:11 +0000</pubDate>
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<title>AudioStory: Generating Long-Form Narrative Audio with Large Language Models</title>
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<link>https://arxiv.org/abs/2508.20088</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20088.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuxin Guo, Teng Wang, Yuying Ge, Shijie Ma, Yixiao Ge, Wei Zou, Ying Shan</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> Recent advances in text-to-audio (TTA) generation excel at synthesizing short audio clips but struggle with long-form narrative audio, which requires temporal coherence and compositional reasoning. To address this gap, we propose AudioStory, a unified framework that integrates large language models (LLMs) with TTA systems to generate structured, long-form audio narratives. AudioStory possesses strong instruction-following reasoning generation capabilities. It employs LLMs to decompose complex narrative queries into temporally ordered sub-tasks with contextual cues, enabling coherent scene transitions and emotional tone consistency. AudioStory has two appealing features: (1) Decoupled bridging mechanism: AudioStory disentangles LLM-diffuser collaboration into two specialized components, i.e., a bridging query for intra-event semantic alignment and a residual query for cross-event coherence preservation. (2) End-to-end training: By unifying instruction comprehension and audio generation within a single end-to-end framework, AudioStory eliminates the need for modular training pipelines while enhancing synergy between components. Furthermore, we establish a benchmark AudioStory-10K, encompassing diverse domains such as animated soundscapes and natural sound narratives. Extensive experiments show the superiority of AudioStory on both single-audio generation and narrative audio generation, surpassing prior TTA baselines in both instruction-following ability and audio fidelity. Our code is available at https://github.com/TencentARC/AudioStory</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20088</guid>
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<pubDate>Wed, 27 Aug 2025 17:55:38 +0000</pubDate>
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<title>CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2508.20096</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20096.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zeyi Sun, Yuhang Cao, Jianze Liang, Qiushi Sun, Ziyu Liu, Zhixiong Zhang, Yuhang Zang, Xiaoyi Dong, Kai Chen, Dahua Lin, Jiaqi Wang</p><p><b>Upvotes:</b> 33</p><p><b>Summary:</b> Autonomous agents for Graphical User Interfaces (GUIs) face significant challenges in specialized domains such as scientific computing, where both long-horizon planning and precise execution are required. Existing approaches suffer from a trade-off: generalist agents excel at planning but perform poorly in execution, while specialized agents demonstrate the opposite weakness. Recent compositional frameworks attempt to bridge this gap by combining a planner and an actor, but they are typically static and non-trainable, which prevents adaptation from experience. This is a critical limitation given the scarcity of high-quality data in scientific domains. To address these limitations, we introduce CODA, a novel and trainable compositional framework that integrates a generalist planner (Cerebrum) with a specialist executor (Cerebellum), trained via a dedicated two-stage pipeline. In the first stage, Specialization, we apply a decoupled GRPO approach to train an expert planner for each scientific application individually, bootstrapping from a small set of task trajectories. In the second stage, Generalization, we aggregate all successful trajectories from the specialized experts to build a consolidated dataset, which is then used for supervised fine-tuning of the final planner. This equips CODA with both robust execution and cross-domain generalization. Evaluated on four challenging applications from the ScienceBoard benchmark, CODA significantly outperforms baselines and establishes a new state of the art among open-source models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20096</guid>
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<pubDate>Wed, 27 Aug 2025 17:59:50 +0000</pubDate>
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<title>TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning</title>
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<link>https://arxiv.org/abs/2508.20374</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20374.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Simin Ma, Shujian Liu, Jun Tan, Yebowen Hu, Song Wang, Sathish Reddy Indurthi, Sanqiang Zhao, Liwei Wu, Jianbing Han, Kaiqiang Song</p><p><b>Upvotes:</b> 19</p><p><b>Summary:</b> Diverse instruction data is vital for effective instruction tuning of large language models, as it enables the model to generalize across different types of inputs . Building such diversified instruction dataset is an essential step in this process. Existing approaches often leverage large language models to automatically explore and generate diverse instructions, ensuring both data diversity and quality. However, they tend to overlook an important factor in real-world applications: on-task relevance. In practice, only a few real-world applications require a truly general-purpose model; most benefit from task-specific knowledge tailored to their particular use case. Therefore, it is vital to develop instruction augmentation methods that not only maintain diversity but are also optimized for specific, real-world scenarios. We thus introduce Task Centric Instruction Augmentation (TCIA), a framework that systematically expands instructions while preserving both diversity and task alignment. By representing instructions in a discrete query-constraints space, TCIA creates a rich set of task-relevant instructions and enables models to generalize to these task-specific instructions without sacrificing overall performance. Experiments show that TCIA improves open-source LLMs' performance by an average of 8.7% across four real-world, task-specific applications, and in some cases outperforming leading closed-source models. These improvements do not compromise general instruction-following ability, making TCIA a scalable and efficient solution for adapting LLMs to real-world, task-focused applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20374</guid>
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<pubDate>Thu, 28 Aug 2025 02:42:10 +0000</pubDate>
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<title>AWorld: Orchestrating the Training Recipe for Agentic AI</title>
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<link>https://arxiv.org/abs/2508.20404</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20404.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chengyue Yu, Siyuan Lu, Chenyi Zhuang, Dong Wang, Qintong Wu, Zongyue Li, Runsheng Gan, Chunfeng Wang, Siqi Hou, Gaochi Huang, Wenlong Yan, Lifeng Hong, Aohui Xue, Yanfeng Wang, Jinjie Gu, David Tsai, Tao Lin</p><p><b>Upvotes:</b> 30</p><p><b>Summary:</b> The learning from practice paradigm is crucial for developing capable Agentic AI systems, yet it is severely hampered by inefficient experience generation, a bottleneck especially pronounced in complex benchmarks like GAIA. To address this, we introduce AWorld, an open-source system engineered for large-scale agent-environment interaction. By distributing tasks across a cluster, AWorld accelerates experience collection by 14.6x compared to standard single-node, sequential execution. This critical speedup makes extensive reinforcement learning practical and scalable. Leveraging this capability, we trained a Qwen3-32B-based agent that significantly outperforms its base model, increasing its overall GAIA accuracy from 21.59% to 32.23%. On the benchmark's most challenging levels, our agent achieves a score of 16.33%, surpassing the performance of leading proprietary models. Our open-source system and resulting agent provide a practical blueprint for a complete agentic AI training pipeline, from efficient interaction to demonstrable model improvement.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20404</guid>
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<pubDate>Thu, 28 Aug 2025 04:04:30 +0000</pubDate>
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<title>MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers</title>
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<link>https://arxiv.org/abs/2508.20453</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20453.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhenting Wang, Qi Chang, Hemani Patel, Shashank Biju, Cheng-En Wu, Quan Liu, Aolin Ding, Alireza Rezazadeh, Ankit Shah, Yujia Bao, Eugene Siow</p><p><b>Upvotes:</b> 41</p><p><b>Summary:</b> We introduce MCP-Bench, a benchmark for evaluating large language models (LLMs) on realistic, multi-step tasks that demand tool use, cross-tool coordination, precise parameter control, and planning/reasoning for solving tasks. Built on the Model Context Protocol (MCP), MCP-Bench connects LLMs to 28 representative live MCP servers spanning 250 tools across domains such as finance, traveling, scientific computing, and academic search. Unlike prior API-based benchmarks, each MCP server provides a set of complementary tools designed to work together, enabling the construction of authentic, multi-step tasks with rich input-output coupling. Tasks in MCP-Bench test agents' ability to retrieve relevant tools from fuzzy instructions without explicit tool names, plan multi-hop execution trajectories for complex objectives, ground responses in intermediate tool outputs, and orchestrate cross-domain workflows - capabilities not adequately evaluated by existing benchmarks that rely on explicit tool specifications, shallow few-step workflows, and isolated domain operations. We propose a multi-faceted evaluation framework covering tool-level schema understanding and usage, trajectory-level planning, and task completion. Experiments on 20 advanced LLMs reveal persistent challenges in MCP-Bench. Code and data: https://github.com/Accenture/mcp-bench.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20453</guid>
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<pubDate>Thu, 28 Aug 2025 05:58:57 +0000</pubDate>
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<title>rStar2-Agent: Agentic Reasoning Technical Report</title>
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<link>https://arxiv.org/abs/2508.20722</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20722.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ning Shang, Yifei Liu, Yi Zhu, Li Lyna Zhang, Weijiang Xu, Xinyu Guan, Buze Zhang, Bingcheng Dong, Xudong Zhou, Bowen Zhang, Ying Xin, Ziming Miao, Scarlett Li, Fan Yang, Mao Yang</p><p><b>Upvotes:</b> 67</p><p><b>Summary:</b> We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstrates advanced cognitive behaviors, such as thinking carefully before using Python coding tools and reflecting on code execution feedback to autonomously explore, verify, and refine intermediate steps in complex problem-solving. This capability is enabled through three key innovations that makes agentic RL effective at scale: (i) an efficient RL infrastructure with a reliable Python code environment that supports high-throughput execution and mitigates the high rollout costs, enabling training on limited GPU resources (64 MI300X GPUs); (ii) GRPO-RoC, an agentic RL algorithm with a Resample-on-Correct rollout strategy that addresses the inherent environment noises from coding tools, allowing the model to reason more effectively in a code environment; (iii) An efficient agent training recipe that starts with non-reasoning SFT and progresses through multi-RL stages, yielding advanced cognitive abilities with minimal compute cost. To this end, rStar2-Agent boosts a pre-trained 14B model to state of the art in only 510 RL steps within one week, achieving average pass@1 scores of 80.6% on AIME24 and 69.8% on AIME25, surpassing DeepSeek-R1 (671B) with significantly shorter responses. Beyond mathematics, rStar2-Agent-14B also demonstrates strong generalization to alignment, scientific reasoning, and agentic tool-use tasks. Code and training recipes are available at https://github.com/microsoft/rStar.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20722</guid>
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<pubDate>Thu, 28 Aug 2025 12:45:25 +0000</pubDate>
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<title>Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2508.20751</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20751.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yibin Wang, Zhimin Li, Yuhang Zang, Yujie Zhou, Jiazi Bu, Chunyu Wang, Qinglin Lu, Cheng Jin, Jiaqi Wang</p><p><b>Upvotes:</b> 75</p><p><b>Summary:</b> Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generation. However, current methods using pointwise reward models (RM) for scoring generated images are susceptible to reward hacking. We reveal that this happens when minimal score differences between images are amplified after normalization, creating illusory advantages that drive the model to over-optimize for trivial gains, ultimately destabilizing the image generation process. To address this, we propose Pref-GRPO, a pairwise preference reward-based GRPO method that shifts the optimization objective from score maximization to preference fitting, ensuring more stable training. In Pref-GRPO, images are pairwise compared within each group using preference RM, and the win rate is used as the reward signal. Extensive experiments demonstrate that PREF-GRPO differentiates subtle image quality differences, providing more stable advantages and mitigating reward hacking. Additionally, existing T2I benchmarks are limited by coarse evaluation criteria, hindering comprehensive model assessment. To solve this, we introduce UniGenBench, a unified T2I benchmark comprising 600 prompts across 5 main themes and 20 subthemes. It evaluates semantic consistency through 10 primary and 27 sub-criteria, leveraging MLLM for benchmark construction and evaluation. Our benchmarks uncover the strengths and weaknesses of both open and closed-source T2I models and validate the effectiveness of Pref-GRPO.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20751</guid>
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<pubDate>Thu, 28 Aug 2025 13:11:24 +0000</pubDate>
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<title>Provable Benefits of In-Tool Learning for Large Language Models</title>
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<link>https://arxiv.org/abs/2508.20755</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20755.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sam Houliston, Ambroise Odonnat, Charles Arnal, Vivien Cabannes</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Tool-augmented language models, equipped with retrieval, memory, or external APIs, are reshaping AI, yet their theoretical advantages remain underexplored. In this paper, we address this question by demonstrating the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall. We show that the number of facts a model can memorize solely in its weights is fundamentally limited by its parameter count. In contrast, we prove that tool-use enables unbounded factual recall via a simple and efficient circuit construction. These results are validated in controlled experiments, where tool-using models consistently outperform memorizing ones. We further show that for pretrained large language models, teaching tool-use and general rules is more effective than finetuning facts into memory. Our work provides both a theoretical and empirical foundation, establishing why tool-augmented workflows are not just practical, but provably more scalable.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20755</guid>
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<pubDate>Thu, 28 Aug 2025 13:12:19 +0000</pubDate>
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<title>Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection</title>
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<link>https://arxiv.org/abs/2508.20766</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.20766.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Harethah Abu Shairah, Hasan Abed Al Kader Hammoud, George Turkiyyah, Bernard Ghanem</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Safety alignment in Large Language Models (LLMs) often involves mediating internal representations to refuse harmful requests. Recent research has demonstrated that these safety mechanisms can be bypassed by ablating or removing specific representational directions within the model. In this paper, we propose the opposite approach: Rank-One Safety Injection (ROSI), a white-box method that amplifies a model's safety alignment by permanently steering its activations toward the refusal-mediating subspace. ROSI operates as a simple, fine-tuning-free rank-one weight modification applied to all residual stream write matrices. The required safety direction can be computed from a small set of harmful and harmless instruction pairs. We show that ROSI consistently increases safety refusal rates - as evaluated by Llama Guard 3 - while preserving the utility of the model on standard benchmarks such as MMLU, HellaSwag, and Arc. Furthermore, we show that ROSI can also re-align 'uncensored' models by amplifying their own latent safety directions, demonstrating its utility as an effective last-mile safety procedure. Our results suggest that targeted, interpretable weight steering is a cheap and potent mechanism to improve LLM safety, complementing more resource-intensive fine-tuning paradigms.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.20766</guid>
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<pubDate>Thu, 28 Aug 2025 13:22:33 +0000</pubDate>
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<title>CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification</title>
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<link>https://arxiv.org/abs/2508.21046</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21046.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wei Li, Renshan Zhang, Rui Shao, Jie He, Liqiang Nie</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Recent Vision-Language-Action (VLA) models built on pre-trained Vision-Language Models (VLMs) require extensive post-training, resulting in high computational overhead that limits scalability and deployment.We propose CogVLA, a Cognition-Aligned Vision-Language-Action framework that leverages instruction-driven routing and sparsification to improve both efficiency and performance. CogVLA draws inspiration from human multimodal coordination and introduces a 3-stage progressive architecture. 1) Encoder-FiLM based Aggregation Routing (EFA-Routing) injects instruction information into the vision encoder to selectively aggregate and compress dual-stream visual tokens, forming a instruction-aware latent representation. 2) Building upon this compact visual encoding, LLM-FiLM based Pruning Routing (LFP-Routing) introduces action intent into the language model by pruning instruction-irrelevant visually grounded tokens, thereby achieving token-level sparsity. 3) To ensure that compressed perception inputs can still support accurate and coherent action generation, we introduce V-L-A Coupled Attention (CAtten), which combines causal vision-language attention with bidirectional action parallel decoding. Extensive experiments on the LIBERO benchmark and real-world robotic tasks demonstrate that CogVLA achieves state-of-the-art performance with success rates of 97.4% and 70.0%, respectively, while reducing training costs by 2.5-fold and decreasing inference latency by 2.8-fold compared to OpenVLA. CogVLA is open-sourced and publicly available at https://github.com/JiuTian-VL/CogVLA.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21046</guid>
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<pubDate>Thu, 28 Aug 2025 17:50:58 +0000</pubDate>
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<title>FakeParts: a New Family of AI-Generated DeepFakes</title>
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<link>https://arxiv.org/abs/2508.21052</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21052.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Gaetan Brison, Soobash Daiboo, Samy Aimeur, Awais Hussain Sani, Xi Wang, Gianni Franchi, Vicky Kalogeiton</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Unlike fully synthetic content, these partial manipulations, ranging from altered facial expressions to object substitutions and background modifications, blend seamlessly with real elements, making them particularly deceptive and difficult to detect. To address the critical gap in detection capabilities, we present FakePartsBench, the first large-scale benchmark dataset specifically designed to capture the full spectrum of partial deepfakes. Comprising over 25K videos with pixel-level and frame-level manipulation annotations, our dataset enables comprehensive evaluation of detection methods. Our user studies demonstrate that FakeParts reduces human detection accuracy by over 30% compared to traditional deepfakes, with similar performance degradation observed in state-of-the-art detection models. This work identifies an urgent vulnerability in current deepfake detection approaches and provides the necessary resources to develop more robust methods for partial video manipulations.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21052</guid>
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<pubDate>Thu, 28 Aug 2025 17:55:14 +0000</pubDate>
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<title>Mixture of Contexts for Long Video Generation</title>
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<link>https://arxiv.org/abs/2508.21058</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21058.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shengqu Cai, Ceyuan Yang, Lvmin Zhang, Yuwei Guo, Junfei Xiao, Ziyan Yang, Yinghao Xu, Zhenheng Yang, Alan Yuille, Leonidas Guibas, Maneesh Agrawala, Lu Jiang, Gordon Wetzstein</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> Long video generation is fundamentally a long context memory problem: models must retain and retrieve salient events across a long range without collapsing or drifting. However, scaling diffusion transformers to generate long-context videos is fundamentally limited by the quadratic cost of self-attention, which makes memory and computation intractable and difficult to optimize for long sequences. We recast long-context video generation as an internal information retrieval task and propose a simple, learnable sparse attention routing module, Mixture of Contexts (MoC), as an effective long-term memory retrieval engine. In MoC, each query dynamically selects a few informative chunks plus mandatory anchors (caption, local windows) to attend to, with causal routing that prevents loop closures. As we scale the data and gradually sparsify the routing, the model allocates compute to salient history, preserving identities, actions, and scenes over minutes of content. Efficiency follows as a byproduct of retrieval (near-linear scaling), which enables practical training and synthesis, and the emergence of memory and consistency at the scale of minutes.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21058</guid>
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<pubDate>Thu, 28 Aug 2025 17:57:55 +0000</pubDate>
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<title>Multi-View 3D Point Tracking</title>
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<link>https://arxiv.org/abs/2508.21060</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21060.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Frano Rajič, Haofei Xu, Marko Mihajlovic, Siyuan Li, Irem Demir, Emircan Gündoğdu, Lei Ke, Sergey Prokudin, Marc Pollefeys, Siyu Tang</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers, which struggle with depth ambiguities and occlusion, or prior multi-camera methods that require over 20 cameras and tedious per-sequence optimization, our feed-forward model directly predicts 3D correspondences using a practical number of cameras (e.g., four), enabling robust and accurate online tracking. Given known camera poses and either sensor-based or estimated multi-view depth, our tracker fuses multi-view features into a unified point cloud and applies k-nearest-neighbors correlation alongside a transformer-based update to reliably estimate long-range 3D correspondences, even under occlusion. We train on 5K synthetic multi-view Kubric sequences and evaluate on two real-world benchmarks: Panoptic Studio and DexYCB, achieving median trajectory errors of 3.1 cm and 2.0 cm, respectively. Our method generalizes well to diverse camera setups of 1-8 views with varying vantage points and video lengths of 24-150 frames. By releasing our tracker alongside training and evaluation datasets, we aim to set a new standard for multi-view 3D tracking research and provide a practical tool for real-world applications. Project page available at https://ethz-vlg.github.io/mvtracker.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21060</guid>
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<pubDate>Thu, 28 Aug 2025 17:58:20 +0000</pubDate>
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<title>OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models</title>
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<link>https://arxiv.org/abs/2508.21061</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21061.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Adam Coscia, Shunan Guo, Eunyee Koh, Alex Endert</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> As multi-turn dialogues with large language models (LLMs) grow longer and more complex, how can users better evaluate and review progress on their conversational goals? We present OnGoal, an LLM chat interface that helps users better manage goal progress. OnGoal provides real-time feedback on goal alignment through LLM-assisted evaluation, explanations for evaluation results with examples, and overviews of goal progression over time, enabling users to navigate complex dialogues more effectively. Through a study with 20 participants on a writing task, we evaluate OnGoal against a baseline chat interface without goal tracking. Using OnGoal, participants spent less time and effort to achieve their goals while exploring new prompting strategies to overcome miscommunication, suggesting tracking and visualizing goals can enhance engagement and resilience in LLM dialogues. Our findings inspired design implications for future LLM chat interfaces that improve goal communication, reduce cognitive load, enhance interactivity, and enable feedback to improve LLM performance.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21061</guid>
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<pubDate>Thu, 28 Aug 2025 17:58:29 +0000</pubDate>
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<title>OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning</title>
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<link>https://arxiv.org/abs/2508.21066</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21066.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuan Gong, Xionghui Wang, Jie Wu, Shiyin Wang, Yitong Wang, Xinglong Wu</p><p><b>Upvotes:</b> 10</p><p><b>Summary:</b> In this paper, we introduce OneReward, a unified reinforcement learning framework that enhances the model's generative capabilities across multiple tasks under different evaluation criteria using only One Reward model. By employing a single vision-language model (VLM) as the generative reward model, which can distinguish the winner and loser for a given task and a given evaluation criterion, it can be effectively applied to multi-task generation models, particularly in contexts with varied data and diverse task objectives. We utilize OneReward for mask-guided image generation, which can be further divided into several sub-tasks such as image fill, image extend, object removal, and text rendering, involving a binary mask as the edit area. Although these domain-specific tasks share same conditioning paradigm, they differ significantly in underlying data distributions and evaluation metrics. Existing methods often rely on task-specific supervised fine-tuning (SFT), which limits generalization and training efficiency. Building on OneReward, we develop Seedream 3.0 Fill, a mask-guided generation model trained via multi-task reinforcement learning directly on a pre-trained base model, eliminating the need for task-specific SFT. Experimental results demonstrate that our unified edit model consistently outperforms both commercial and open-source competitors, such as Ideogram, Adobe Photoshop, and FLUX Fill [Pro], across multiple evaluation dimensions. Code and model are available at: https://one-reward.github.io</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21066</guid>
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<pubDate>Thu, 28 Aug 2025 17:59:46 +0000</pubDate>
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<title>Dress&Dance: Dress up and Dance as You Like It - Technical Preview</title>
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<link>https://arxiv.org/abs/2508.21070</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.21070.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jun-Kun Chen, Aayush Bansal, Minh Phuoc Vo, Yu-Xiong Wang</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> We present Dress&Dance, a video diffusion framework that generates high quality 5-second-long 24 FPS virtual try-on videos at 1152x720 resolution of a user wearing desired garments while moving in accordance with a given reference video. Our approach requires a single user image and supports a range of tops, bottoms, and one-piece garments, as well as simultaneous tops and bottoms try-on in a single pass. Key to our framework is CondNet, a novel conditioning network that leverages attention to unify multi-modal inputs (text, images, and videos), thereby enhancing garment registration and motion fidelity. CondNet is trained on heterogeneous training data, combining limited video data and a larger, more readily available image dataset, in a multistage progressive manner. Dress&Dance outperforms existing open source and commercial solutions and enables a high quality and flexible try-on experience.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.21070</guid>
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<pubDate>Thu, 28 Aug 2025 17:59:55 +0000</pubDate>
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