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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>Fri, 08 Aug 2025 00:02:41 +0000</lastBuildDate>
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<lastBuildDate>Sat, 09 Aug 2025 00:02:28 +0000</lastBuildDate>
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<title>Efficient Agents: Building Effective Agents While Reducing Cost</title>
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<title>StrandDesigner: Towards Practical Strand Generation with Sketch Guidance</title>
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<link>https://arxiv.org/abs/2508.02694</link>
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<link>https://arxiv.org/abs/2508.01650</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02694.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ningning Wang, Xavier Hu, Pai Liu, He Zhu, Yue Hou, Heyuan Huang, Shengyu Zhang, Jian Yang, Jiaheng Liu, Ge Zhang, Changwang Zhang, Jun Wang, Yuchen Eleanor Jiang, Wangchunshu Zhou</p><p><b>Upvotes:</b> 48</p><p><b>Summary:</b> The remarkable capabilities of Large Language Model (LLM)-driven agents have enabled sophisticated systems to tackle complex, multi-step tasks, but their escalating costs threaten scalability and accessibility. This work presents the first systematic study of the efficiency-effectiveness trade-off in modern agent systems, addressing the critical need for cost-effective designs without sacrificing performance. We investigate three key questions: (1) How much complexity do agentic tasks inherently require? (2) When do additional modules yield diminishing returns? (3) How much efficiency can be gained through the design of efficient agent frameworks? Through an empirical analysis on the GAIA benchmark, we evaluate the impact of LLM backbone selection, agent framework designs, and test-time scaling strategies. Using the cost-of-pass metric, we quantify the efficiency-performance trade-off across these dimensions. Our findings inform the development of Efficient Agents , a novel agent framework that has an optimal complexity to task requirements. Efficient Agents retains 96.7% of the performance of OWL, one leading open-source agent framework, while reducing operational costs from 0.398 to 0.228, resulting in a 28.4% improvement in cost-of-pass. Our work provides actionable insights for designing efficient, high-performing agent systems, advancing the accessibility and sustainability of AI-driven solutions.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01650.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Na Zhang, Moran Li, Chengming Xu, Han Feng, Xiaobin Hu, Jiangning Zhang, Weijian Cao, Chengjie Wang, Yanwei Fu</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Realistic hair strand generation is crucial for applications like computer graphics and virtual reality. While diffusion models can generate hairstyles from text or images, these inputs lack precision and user-friendliness. Instead, we propose the first sketch-based strand generation model, which offers finer control while remaining user-friendly. Our framework tackles key challenges, such as modeling complex strand interactions and diverse sketch patterns, through two main innovations: a learnable strand upsampling strategy that encodes 3D strands into multi-scale latent spaces, and a multi-scale adaptive conditioning mechanism using a transformer with diffusion heads to ensure consistency across granularity levels. Experiments on several benchmark datasets show our method outperforms existing approaches in realism and precision. Qualitative results further confirm its effectiveness. Code will be released at [GitHub](https://github.com/fighting-Zhang/StrandDesigner).</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02694</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01650</guid>
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<pubDate>Thu, 24 Jul 2025 17:56:51 +0000</pubDate>
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<pubDate>Sun, 03 Aug 2025 08:17:50 +0000</pubDate>
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<title>Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks</title>
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<title>Marco-Voice Technical Report</title>
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<link>https://arxiv.org/abs/2507.21974</link>
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<link>https://arxiv.org/abs/2508.02038</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.21974.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mohamed Sana, Nicola Piovesan, Antonio De Domenico, Yibin Kang, Haozhe Zhang, Merouane Debbah, Fadhel Ayed</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lightweight framework that leverages Large Language Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of annotated troubleshooting problems designed to benchmark RCA capabilities. Our evaluation reveals that existing open-source reasoning LLMs struggle with these problems, underscoring the need for domain-specific adaptation. To address this issue, we propose a two-stage training methodology that combines supervised fine-tuning with reinforcement learning to improve the accuracy and reasoning quality of LLMs. The proposed approach fine-tunes a series of RCA models to integrate domain knowledge and generate structured, multi-step diagnostic explanations, improving both interpretability and effectiveness. Extensive experiments across multiple LLM sizes show significant performance gains over state-of-the-art reasoning and non-reasoning models, including strong generalization to randomized test variants. These results demonstrate the promise of domain-adapted, reasoning-enhanced LLMs for practical and explainable RCA in network operation and management.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02038.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fengping Tian, Chenyang Lyu, Xuanfan Ni, Haoqin Sun, Qingjuan Li, Zhiqiang Qian, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo, Kaifu Zhang</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> This paper presents a multifunctional speech synthesis system that integrates voice cloning and emotion control speech synthesis within a unified framework. The goal of this work is to address longstanding challenges in achieving highly expressive, controllable, and natural speech generation that faithfully preserves speaker identity across diverse linguistic and emotional contexts. Our approach introduces an effective speaker-emotion disentanglement mechanism with in-batch contrastive learning, enabling independent manipulation of speaker identity and eemotional style, as well as rotational emotional embedding integration method for smooth emotion control. To support comprehensive training and evaluation, we construct CSEMOTIONS, a high-quality emotional speech dataset containing 10 hours of Mandarin speech from six professional speakers across seven emotional categories. Extensive experiments demonstrate that our system, Marco-Voice, achieves substantial improvements in both objective and subjective metrics. Comprehensive evaluations and analysis were conducted, results show that MarcoVoice delivers competitive performance in terms of speech clarity and emotional richness, representing a substantial advance in the field of expressive neural speech synthesis.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2507.21974</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02038</guid>
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<pubDate>Tue, 29 Jul 2025 16:21:42 +0000</pubDate>
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<pubDate>Mon, 04 Aug 2025 04:08:22 +0000</pubDate>
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<title>The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models</title>
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<title>Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models</title>
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<link>https://arxiv.org/abs/2507.23313</link>
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<link>https://arxiv.org/abs/2508.02120</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.23313.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Alfio Ferrara, Sergio Picascia, Elisabetta Rocchetti</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Text-to-image diffusion models have demonstrated remarkable capabilities in generating artistic content by learning from billions of images, including popular artworks. However, the fundamental question of how these models internally represent concepts, such as content and style in paintings, remains unexplored. Traditional computer vision assumes content and style are orthogonal, but diffusion models receive no explicit guidance about this distinction during training. In this work, we investigate how transformer-based text-to-image diffusion models encode content and style concepts when generating artworks. We leverage cross-attention heatmaps to attribute pixels in generated images to specific prompt tokens, enabling us to isolate image regions influenced by content-describing versus style-describing tokens. Our findings reveal that diffusion models demonstrate varying degrees of content-style separation depending on the specific artistic prompt and style requested. In many cases, content tokens primarily influence object-related regions while style tokens affect background and texture areas, suggesting an emergent understanding of the content-style distinction. These insights contribute to our understanding of how large-scale generative models internally represent complex artistic concepts without explicit supervision. We share the code and dataset, together with an exploratory tool for visualizing attention maps at https://github.com/umilISLab/artistic-prompt-interpretation.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02120.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Linan Yue, Yichao Du, Yizhi Wang, Weibo Gao, Fangzhou Yao, Li Wang, Ye Liu, Ziyu Xu, Qi Liu, Shimin Di, Min-Ling Zhang</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Recently, Large Reasoning Models (LRMs) have gradually become a research hotspot due to their outstanding performance in handling complex tasks. Among them, DeepSeek R1 has garnered significant attention for its exceptional performance and open-source nature, driving advancements in the research of R1-style LRMs. Unlike traditional Large Language Models (LLMs), these models enhance logical deduction and decision-making capabilities during reasoning by incorporating mechanisms such as long chain-of-thought and self-reflection through reinforcement learning. However, with the widespread application of these models, the problem of overthinking has gradually emerged. Specifically, when generating answers, these models often construct excessively long reasoning chains with redundant or repetitive steps, which leads to reduced reasoning efficiency and may affect the accuracy of the final answer. To this end, various efficient reasoning methods have been proposed, aiming to reduce the length of reasoning paths without compromising model performance and reasoning capability. By reviewing the current research advancements in the field of efficient reasoning methods systematically, we categorize existing works into two main directions based on the lens of single-model optimization versus model collaboration: (1) Efficient Reasoning with Single Model, which focuses on improving the reasoning efficiency of individual models; and (2) Efficient Reasoning with Model Collaboration, which explores optimizing reasoning paths through collaboration among multiple models. Besides, we maintain a public GitHub repository that tracks the latest progress in efficient reasoning methods.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2507.23313</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02120</guid>
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<pubDate>Thu, 31 Jul 2025 07:47:01 +0000</pubDate>
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<pubDate>Mon, 04 Aug 2025 06:54:31 +0000</pubDate>
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<title>Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis</title>
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<title>I2CR: Intra- and Inter-modal Collaborative Reflections for Multimodal Entity Linking</title>
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<link>https://arxiv.org/abs/2507.23785</link>
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<link>https://arxiv.org/abs/2508.02243</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.23785.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bowen Zhang, Sicheng Xu, Chuxin Wang, Jiaolong Yang, Feng Zhao, Dong Chen, Baining Guo</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset, our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02243.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ziyan Liu, Junwen Li, Kaiwen Li, Tong Ruan, Chao Wang, Xinyan He, Zongyu Wang, Xuezhi Cao, Jingping Liu</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Multimodal entity linking plays a crucial role in a wide range of applications. Recent advances in large language model-based methods have become the dominant paradigm for this task, effectively leveraging both textual and visual modalities to enhance performance. Despite their success, these methods still face two challenges, including unnecessary incorporation of image data in certain scenarios and the reliance only on a one-time extraction of visual features, which can undermine their effectiveness and accuracy. To address these challenges, we propose a novel LLM-based framework for the multimodal entity linking task, called Intra- and Inter-modal Collaborative Reflections. This framework prioritizes leveraging text information to address the task. When text alone is insufficient to link the correct entity through intra- and inter-modality evaluations, it employs a multi-round iterative strategy that integrates key visual clues from various aspects of the image to support reasoning and enhance matching accuracy. Extensive experiments on three widely used public datasets demonstrate that our framework consistently outperforms current state-of-the-art methods in the task, achieving improvements of 3.2%, 5.1%, and 1.6%, respectively. Our code is available at https://github.com/ziyan-xiaoyu/I2CR/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2507.23785</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02243</guid>
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<pubDate>Thu, 31 Jul 2025 17:59:51 +0000</pubDate>
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<pubDate>Mon, 04 Aug 2025 09:43:54 +0000</pubDate>
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<title>FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality</title>
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<title>Visual Document Understanding and Question Answering: A Multi-Agent Collaboration Framework with Test-Time Scaling</title>
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<link>https://arxiv.org/abs/2508.00109</link>
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<link>https://arxiv.org/abs/2508.03404</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00109.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mingda Chen, Yang Li, Xilun Chen, Adina Williams, Gargi Ghosh, Scott Yih</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, leading to potential quality issues. To address this limitation, we introduce FACTORY, a large-scale, human-verified prompt set. Developed using a model-in-the-loop approach and refined by humans, FACTORY includes challenging prompts that are fact-seeking, answerable, and unambiguous. We conduct human evaluations on 6 state-of-the-art language models using FACTORY and existing datasets. Our results show that FACTORY is a challenging benchmark: approximately 40% of the claims made in the responses of SOTA models are not factual, compared to only 10% for other datasets. Our analysis identifies the strengths of FACTORY over prior benchmarks, emphasizing its reliability and the necessity for models to reason across long-tailed facts.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03404.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xinlei Yu, Zhangquan Chen, Yudong Zhang, Shilin Lu, Ruolin Shen, Jiangning Zhang, Xiaobin Hu, Yanwei Fu, Shuicheng Yan</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Existing vision-language models (VLMs), whether generalists or specialists, remain constrained by their parameter scale, lack robust self-correction capabilities, and underperform in tasks involving long visual contexts and complex reasoning, resulting in suboptimal performance on document-based tasks. To address this, we propose MACT, a Multi-Agent Collaboration framework with Test-Time scaling, tailored for visual document understanding and visual question answering (VQA). It comprises four distinct small-scale agents, i.e., planning, execution, judgment, and answer agents, with clearly defined roles and effective collaboration. Notably, the judgment agent exclusively verifies correctness and redirects to prior agents for revisions, outperforming conventional correction strategies. To further expand the capability boundaries of the framework, we propose mixed reward modeling that balances agent-specific abilities and global collaboration, as well as agent-wise hybrid test-time scaling, which customizes different scaling strategies for each agent based on their functions. Evaluated on benchmarks spanning both document-based and non-document-based settings, our MACT shows superior performance with a smaller parameter scale without sacrificing the ability of general and mathematical tasks. Especially, it stands out in benchmarks involving long visual contexts and complicated reasoning. The three variants of MACT consistently hold the top three positions in average scores, leading in 13 of the 15 benchmarks. Code will be available at: https://github.com/YU-deep/MACT.git.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.00109</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03404</guid>
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<pubDate>Thu, 31 Jul 2025 19:00:11 +0000</pubDate>
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<pubDate>Tue, 05 Aug 2025 12:52:09 +0000</pubDate>
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<title>RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization</title>
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<title>Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?</title>
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<link>https://arxiv.org/abs/2508.00222</link>
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<link>https://arxiv.org/abs/2508.03644</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00222.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei Ma, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). However, it struggles to break through the inherent capability boundaries of the base LLM, due to its essentially on-policy strategy coupled with LLM's immense action space and sparse reward. Critically, RLVR can lead to the capability boundary collapse, narrowing the LLM's problem-solving scope. To address this problem, we propose RL-PLUS, a novel hybrid-policy optimization approach for LLMs that synergizes internal exploitation with external data to achieve stronger reasoning capabilities and surpass the boundaries of base models. RL-PLUS integrates two core components, i.e., Multiple Importance Sampling to address distributional mismatch from external data, and Exploration-Based Advantage Function to guide the model towards high-value, unexplored reasoning paths. We provide both theoretical analysis and extensive experiments to demonstrate the superiority and generalizability of our approach. Compared with existing RLVR methods, RL-PLUS achieves 1) state-of-the-art performance on six math reasoning benchmarks; 2) superior performance on six out-of-distribution reasoning tasks; 3) consistent and significant gains across diverse model families, with average relative improvements up to 69.2\%. Moreover, the analysis of Pass@k curves indicates that RL-PLUS effectively resolves the capability boundary collapse problem.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03644.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wenxuan Shen, Mingjia Wang, Yaochen Wang, Dongping Chen, Junjie Yang, Yao Wan, Weiwei Lin</p><p><b>Upvotes:</b> 13</p><p><b>Summary:</b> Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language Models (MLLMs) show great promise for complex document understanding, yet their development is critically hampered by inadequate evaluation. Current benchmarks often focus on specific part of document RAG system and use synthetic data with incomplete ground truth and evidence labels, therefore failing to reflect real-world bottlenecks and challenges. To overcome these limitations, we introduce Double-Bench: a new large-scale, multilingual, and multimodal evaluation system that is able to produce fine-grained assessment to each component within document RAG systems. It comprises 3,276 documents (72,880 pages) and 5,168 single- and multi-hop queries across 6 languages and 4 document types with streamlined dynamic update support for potential data contamination issues. Queries are grounded in exhaustively scanned evidence pages and verified by human experts to ensure maximum quality and completeness. Our comprehensive experiments across 9 state-of-the-art embedding models, 4 MLLMs and 4 end-to-end document RAG frameworks demonstrate the gap between text and visual embedding models is narrowing, highlighting the need in building stronger document retrieval models. Our findings also reveal the over-confidence dilemma within current document RAG frameworks that tend to provide answer even without evidence support. We hope our fully open-source Double-Bench provide a rigorous foundation for future research in advanced document RAG systems. We plan to retrieve timely corpus and release new benchmarks on an annual basis.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.00222</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03644</guid>
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<pubDate>Thu, 31 Jul 2025 23:55:29 +0000</pubDate>
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<pubDate>Tue, 05 Aug 2025 16:55:02 +0000</pubDate>
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<title>Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation</title>
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<title>CoAct-1: Computer-using Agents with Coding as Actions</title>
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<link>https://arxiv.org/abs/2508.00428</link>
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<link>https://arxiv.org/abs/2508.03923</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00428.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Nan Xiang, Tianyi Liang, Haiwen Huang, Shiqi Jiang, Hao Huang, Yifei Huang, Liangyu Chen, Changbo Wang, Chenhui Li</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Text-to-3D (T23D) generation has transformed digital content creation, yet remains bottlenecked by blind trial-and-error prompting processes that yield unpredictable results. While visual prompt engineering has advanced in text-to-image domains, its application to 3D generation presents unique challenges requiring multi-view consistency evaluation and spatial understanding. We present Sel3DCraft, a visual prompt engineering system for T23D that transforms unstructured exploration into a guided visual process. Our approach introduces three key innovations: a dual-branch structure combining retrieval and generation for diverse candidate exploration; a multi-view hybrid scoring approach that leverages MLLMs with innovative high-level metrics to assess 3D models with human-expert consistency; and a prompt-driven visual analytics suite that enables intuitive defect identification and refinement. Extensive testing and user studies demonstrate that Sel3DCraft surpasses other T23D systems in supporting creativity for designers.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03923.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Linxin Song, Yutong Dai, Viraj Prabhu, Jieyu Zhang, Taiwei Shi, Li Li, Junnan Li, Silvio Savarese, Zeyuan Chen, Jieyu Zhao, Ran Xu, Caiming Xiong</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Autonomous agents that operate computers via Graphical User Interfaces (GUIs) often struggle with efficiency and reliability on complex, long-horizon tasks. While augmenting these agents with planners can improve task decomposition, they remain constrained by the inherent limitations of performing all actions through GUI manipulation, leading to brittleness and inefficiency. In this work, we introduce a more robust and flexible paradigm: enabling agents to use coding as a enhanced action. We present CoAct-1, a novel multi-agent system that synergistically combines GUI-based control with direct programmatic execution. CoAct-1 features an Orchestrator that dynamically delegates subtasks to either a conventional GUI Operator or a specialized Programmer agent, which can write and execute Python or Bash scripts. This hybrid approach allows the agent to bypass inefficient GUI action sequences for tasks like file management and data processing, while still leveraging visual interaction when necessary. We evaluate our system on the challenging OSWorld benchmark, where CoAct-1 achieves a new state-of-the-art success rate of 60.76%, significantly outperforming prior methods. Furthermore, our approach dramatically improves efficiency, reducing the average number of steps required to complete a task to just 10.15, compared to 15 for leading GUI agents. Our results demonstrate that integrating coding as a core action provides a more powerful, efficient, and scalable path toward generalized computer automation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03923</guid>
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<pubDate>Fri, 01 Aug 2025 08:36:15 +0000</pubDate>
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<pubDate>Tue, 05 Aug 2025 21:33:36 +0000</pubDate>
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<title>DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior</title>
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<title>Are Today's LLMs Ready to Explain Well-Being Concepts?</title>
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<link>https://arxiv.org/abs/2508.00599</link>
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<link>https://arxiv.org/abs/2508.03990</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00599.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Junzhe Lu, Jing Lin, Hongkun Dou, Ailing Zeng, Yue Deng, Xian Liu, Zhongang Cai, Lei Yang, Yulun Zhang, Haoqian Wang, Ziwei Liu</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> We present DPoser-X, a diffusion-based prior model for 3D whole-body human poses. Building a versatile and robust full-body human pose prior remains challenging due to the inherent complexity of articulated human poses and the scarcity of high-quality whole-body pose datasets. To address these limitations, we introduce a Diffusion model as body Pose prior (DPoser) and extend it to DPoser-X for expressive whole-body human pose modeling. Our approach unifies various pose-centric tasks as inverse problems, solving them through variational diffusion sampling. To enhance performance on downstream applications, we introduce a novel truncated timestep scheduling method specifically designed for pose data characteristics. We also propose a masked training mechanism that effectively combines whole-body and part-specific datasets, enabling our model to capture interdependencies between body parts while avoiding overfitting to specific actions. Extensive experiments demonstrate DPoser-X's robustness and versatility across multiple benchmarks for body, hand, face, and full-body pose modeling. Our model consistently outperforms state-of-the-art alternatives, establishing a new benchmark for whole-body human pose prior modeling.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03990.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bohan Jiang, Dawei Li, Zhen Tan, Chengshuai Zhao, Huan Liu</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Well-being encompasses mental, physical, and social dimensions essential to personal growth and informed life decisions. As individuals increasingly consult Large Language Models (LLMs) to understand well-being, a key challenge emerges: Can LLMs generate explanations that are not only accurate but also tailored to diverse audiences? High-quality explanations require both factual correctness and the ability to meet the expectations of users with varying expertise. In this work, we construct a large-scale dataset comprising 43,880 explanations of 2,194 well-being concepts, generated by ten diverse LLMs. We introduce a principle-guided LLM-as-a-judge evaluation framework, employing dual judges to assess explanation quality. Furthermore, we show that fine-tuning an open-source LLM using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) can significantly enhance the quality of generated explanations. Our results reveal: (1) The proposed LLM judges align well with human evaluations; (2) explanation quality varies significantly across models, audiences, and categories; and (3) DPO- and SFT-finetuned models outperform their larger counterparts, demonstrating the effectiveness of preference-based learning for specialized explanation tasks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.00599</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03990</guid>
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<pubDate>Fri, 01 Aug 2025 12:56:39 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 00:45:02 +0000</pubDate>
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<title>Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens</title>
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<title>Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability</title>
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<link>https://arxiv.org/abs/2508.01191</link>
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<link>https://arxiv.org/abs/2508.04017</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01191.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu</p><p><b>Upvotes:</b> 108</p><p><b>Summary:</b> Chain-of-Thought (CoT) prompting has been shown to improve Large Language Model (LLM) performance on various tasks. With this approach, LLMs appear to produce human-like reasoning steps before providing answers (a.k.a., CoT reasoning), which often leads to the perception that they engage in deliberate inferential processes. However, some initial findings suggest that CoT reasoning may be more superficial than it appears, motivating us to explore further. In this paper, we study CoT reasoning via a data distribution lens and investigate if CoT reasoning reflects a structured inductive bias learned from in-distribution data, allowing the model to conditionally generate reasoning paths that approximate those seen during training. Thus, its effectiveness is fundamentally bounded by the degree of distribution discrepancy between the training data and the test queries. With this lens, we dissect CoT reasoning via three dimensions: task, length, and format. To investigate each dimension, we design DataAlchemy, an isolated and controlled environment to train LLMs from scratch and systematically probe them under various distribution conditions. Our results reveal that CoT reasoning is a brittle mirage that vanishes when it is pushed beyond training distributions. This work offers a deeper understanding of why and when CoT reasoning fails, emphasizing the ongoing challenge of achieving genuine and generalizable reasoning.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04017.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haiqi Yang, Jinzhe Li, Gengxu Li, Yi Chang, Yuan Wu</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Large Multimodal Models (LMMs) have witnessed remarkable growth, showcasing formidable capabilities in handling intricate multimodal tasks with exceptional performance. Recent research has underscored the inclination of large language models to passively accept defective inputs, often resulting in futile reasoning on invalid prompts. However, the same critical question of whether LMMs can actively detect and scrutinize erroneous inputs still remains unexplored. To address this gap, we introduce the Input Scrutiny Ability Evaluation Framework (ISEval), which encompasses seven categories of flawed premises and three evaluation metrics. Our extensive evaluation of ten advanced LMMs has identified key findings. Most models struggle to actively detect flawed textual premises without guidance, which reflects a strong reliance on explicit prompts for premise error identification. Error type affects performance: models excel at identifying logical fallacies but struggle with surface-level linguistic errors and certain conditional flaws. Modality trust varies-Gemini 2.5 pro and Claude Sonnet 4 balance visual and textual info, while aya-vision-8b over-rely on text in conflicts. These insights underscore the urgent need to enhance LMMs' proactive verification of input validity and shed novel insights into mitigating the problem. The code is available at https://github.com/MLGroupJLU/LMM_ISEval.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01191</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04017</guid>
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<pubDate>Sat, 02 Aug 2025 04:37:28 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 02:13:46 +0000</pubDate>
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<title>A Coarse-to-Fine Approach to Multi-Modality 3D Occupancy Grounding</title>
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<title>Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decode</title>
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<link>https://arxiv.org/abs/2508.01197</link>
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<link>https://arxiv.org/abs/2508.04107</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01197.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhan Shi, Song Wang, Junbo Chen, Jianke Zhu</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Visual grounding aims to identify objects or regions in a scene based on natural language descriptions, essential for spatially aware perception in autonomous driving. However, existing visual grounding tasks typically depend on bounding boxes that often fail to capture fine-grained details. Not all voxels within a bounding box are occupied, resulting in inaccurate object representations. To address this, we introduce a benchmark for 3D occupancy grounding in challenging outdoor scenes. Built on the nuScenes dataset, it integrates natural language with voxel-level occupancy annotations, offering more precise object perception compared to the traditional grounding task. Moreover, we propose GroundingOcc, an end-to-end model designed for 3D occupancy grounding through multi-modal learning. It combines visual, textual, and point cloud features to predict object location and occupancy information from coarse to fine. Specifically, GroundingOcc comprises a multimodal encoder for feature extraction, an occupancy head for voxel-wise predictions, and a grounding head to refine localization. Additionally, a 2D grounding module and a depth estimation module enhance geometric understanding, thereby boosting model performance. Extensive experiments on the benchmark demonstrate that our method outperforms existing baselines on 3D occupancy grounding. The dataset is available at https://github.com/RONINGOD/GroundingOcc.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04107.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jingchao Wang, Zhijian Wu, Dingjiang Huang, Yefeng Zheng, Hong Wang</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Reference Expression Segmentation (RES) aims to segment image regions specified by referring expressions and has become popular with the rise of multimodal large models (MLLMs). While MLLMs excel in semantic understanding, their token-generation paradigm struggles with pixel-level dense prediction. Existing RES methods either couple MLLMs with the parameter-heavy Segment Anything Model (SAM) with 632M network parameters or adopt SAM-free lightweight pipelines that sacrifice accuracy. To address the trade-off between performance and cost, we specifically propose MLLMSeg, a novel framework that fully exploits the inherent visual detail features encoded in the MLLM vision encoder without introducing an extra visual encoder. Besides, we propose a detail-enhanced and semantic-consistent feature fusion module (DSFF) that fully integrates the detail-related visual feature with the semantic-related feature output by the large language model (LLM) of MLLM. Finally, we establish a light-weight mask decoder with only 34M network parameters that optimally leverages detailed spatial features from the visual encoder and semantic features from the LLM to achieve precise mask prediction. Extensive experiments demonstrate that our method generally surpasses both SAM-based and SAM-free competitors, striking a better balance between performance and cost. Code is available at https://github.com/jcwang0602/MLLMSeg.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01197</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04107</guid>
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<pubDate>Sat, 02 Aug 2025 05:05:50 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 06:06:52 +0000</pubDate>
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<title>C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor</title>
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<title>RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation</title>
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<link>https://arxiv.org/abs/2508.01311</link>
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<link>https://arxiv.org/abs/2508.04190</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01311.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haoquan Lu, Hanzhe Liang, Jie Zhang, Chenxi Hu, Jinbao Wang, Can Gao</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> 3D Anomaly Detection (AD) has shown great potential in detecting anomalies or defects of high-precision industrial products. However, existing methods are typically trained in a class-specific manner and also lack the capability of learning from emerging classes. In this study, we proposed a continual learning framework named Continual 3D Anomaly Detection (C3D-AD), which can not only learn generalized representations for multi-class point clouds but also handle new classes emerging over time.Specifically, in the feature extraction module, to extract generalized local features from diverse product types of different tasks efficiently, Kernel Attention with random feature Layer (KAL) is introduced, which normalizes the feature space. Then, to reconstruct data correctly and continually, an efficient Kernel Attention with learnable Advisor (KAA) mechanism is proposed, which learns the information from new categories while discarding redundant old information within both the encoder and decoder. Finally, to keep the representation consistency over tasks, a Reconstruction with Parameter Perturbation (RPP) module is proposed by designing a representation rehearsal loss function, which ensures that the model remembers previous category information and returns category-adaptive representation.Extensive experiments on three public datasets demonstrate the effectiveness of the proposed method, achieving an average performance of 66.4%, 83.1%, and 63.4% AUROC on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, respectively.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04190.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fengyi Wu, Yimian Dai, Tianfang Zhang, Yixuan Ding, Jian Yang, Ming-Ming Cheng, Zhenming Peng</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Robust principal component analysis (RPCA) decomposes an observation matrix into low-rank background and sparse object components. This capability has enabled its application in tasks ranging from image restoration to segmentation. However, traditional RPCA models suffer from computational burdens caused by matrix operations, reliance on finely tuned hyperparameters, and rigid priors that limit adaptability in dynamic scenarios. To solve these limitations, we propose RPCANet++, a sparse object segmentation framework that fuses the interpretability of RPCA with efficient deep architectures. Our approach unfolds a relaxed RPCA model into a structured network comprising a Background Approximation Module (BAM), an Object Extraction Module (OEM), and an Image Restoration Module (IRM). To mitigate inter-stage transmission loss in the BAM, we introduce a Memory-Augmented Module (MAM) to enhance background feature preservation, while a Deep Contrast Prior Module (DCPM) leverages saliency cues to expedite object extraction. Extensive experiments on diverse datasets demonstrate that RPCANet++ achieves state-of-the-art performance under various imaging scenarios. We further improve interpretability via visual and numerical low-rankness and sparsity measurements. By combining the theoretical strengths of RPCA with the efficiency of deep networks, our approach sets a new baseline for reliable and interpretable sparse object segmentation. Codes are available at our Project Webpage https://fengyiwu98.github.io/rpcanetx.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01311</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04190</guid>
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<pubDate>Sat, 02 Aug 2025 10:54:55 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 08:19:37 +0000</pubDate>
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<title>OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets</title>
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<title>Evaluating, Synthesizing, and Enhancing for Customer Support Conversation</title>
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<link>https://arxiv.org/abs/2508.01630</link>
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<link>https://arxiv.org/abs/2508.04423</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01630.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Maziyar Panahi</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Named-entity recognition (NER) is fundamental to extracting structured information from the >80% of healthcare data that resides in unstructured clinical notes and biomedical literature. Despite recent advances with large language models, achieving state-of-the-art performance across diverse entity types while maintaining computational efficiency remains a significant challenge. We introduce OpenMed NER, a suite of open-source, domain-adapted transformer models that combine lightweight domain-adaptive pre-training (DAPT) with parameter-efficient Low-Rank Adaptation (LoRA). Our approach performs cost-effective DAPT on a 350k-passage corpus compiled from ethically sourced, publicly available research repositories and de-identified clinical notes (PubMed, arXiv, and MIMIC-III) using DeBERTa-v3, PubMedBERT, and BioELECTRA backbones. This is followed by task-specific fine-tuning with LoRA, which updates less than 1.5% of model parameters. We evaluate our models on 12 established biomedical NER benchmarks spanning chemicals, diseases, genes, and species. OpenMed NER achieves new state-of-the-art micro-F1 scores on 10 of these 12 datasets, with substantial gains across diverse entity types. Our models advance the state-of-the-art on foundational disease and chemical benchmarks (e.g., BC5CDR-Disease, +2.70 pp), while delivering even larger improvements of over 5.3 and 9.7 percentage points on more specialized gene and clinical cell line corpora. This work demonstrates that strategically adapted open-source models can surpass closed-source solutions. This performance is achieved with remarkable efficiency: training completes in under 12 hours on a single GPU with a low carbon footprint (< 1.2 kg CO2e), producing permissively licensed, open-source checkpoints designed to help practitioners facilitate compliance with emerging data protection and AI regulations, such as the EU AI Act.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04423.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jie Zhu, Huaixia Dou, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang, Fang Kong</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service agents to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer-agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution. All code and data will be made publicly available at https://github.com/aliyun/qwen-dianjin.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01630</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04423</guid>
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<pubDate>Sun, 03 Aug 2025 07:33:28 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 13:11:17 +0000</pubDate>
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<title>DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion</title>
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<title>Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis</title>
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||||||
<link>https://arxiv.org/abs/2508.01778</link>
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<link>https://arxiv.org/abs/2508.04699</link>
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||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01778.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhigang Sun, Yiru Wang, Anqing Jiang, Shuo Wang, Yu Gao, Yuwen Heng, Shouyi Zhang, An He, Hao Jiang, Jinhao Chai, Zichong Gu, Wang Jijun, Shichen Tang, Lavdim Halilaj, Juergen Luettin, Hao Sun</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-based representations are well-suited to vision models but lack geometric precision, while graph-based representations retain structural detail but become unstable without precise maps. To harness the complementary strengths of both, we propose DiffSemanticFusion -- a fusion framework for multimodal trajectory prediction and planning. Our approach reasons over a semantic raster-fused BEV space, enhanced by a map diffusion module that improves both the stability and expressiveness of online HD map representations. We validate our framework on two downstream tasks: trajectory prediction and planning-oriented end-to-end autonomous driving. Experiments on real-world autonomous driving benchmarks, nuScenes and NAVSIM, demonstrate improved performance over several state-of-the-art methods. For the prediction task on nuScenes, we integrate DiffSemanticFusion with the online HD map informed QCNet, achieving a 5.1\% performance improvement. For end-to-end autonomous driving in NAVSIM, DiffSemanticFusion achieves state-of-the-art results, with a 15\% performance gain in NavHard scenarios. In addition, extensive ablation and sensitivity studies show that our map diffusion module can be seamlessly integrated into other vector-based approaches to enhance performance. All artifacts are available at https://github.com/SunZhigang7/DiffSemanticFusion.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04699.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Anushka Yadav, Isha Nalawade, Srujana Pillarichety, Yashwanth Babu, Reshmi Ghosh, Samyadeep Basu, Wenlong Zhao, Ali Nasaeh, Sriram Balasubramanian, Soundararajan Srinivasan</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> The emergence of reasoning models and their integration into practical AI chat bots has led to breakthroughs in solving advanced math, deep search, and extractive question answering problems that requires a complex and multi-step thought process. Yet, a complete understanding of why these models hallucinate more than general purpose language models is missing. In this investigative study, we systematicallyexplore reasoning failures of contemporary language models on multi-hop question answering tasks. We introduce a novel, nuanced error categorization framework that examines failures across three critical dimensions: the diversity and uniqueness of source documents involved ("hops"), completeness in capturing relevant information ("coverage"), and cognitive inefficiency ("overthinking"). Through rigorous hu-man annotation, supported by complementary automated metrics, our exploration uncovers intricate error patterns often hidden by accuracy-centric evaluations. This investigative approach provides deeper insights into the cognitive limitations of current models and offers actionable guidance toward enhancing reasoning fidelity, transparency, and robustness in future language modeling efforts.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01778</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04699</guid>
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<pubDate>Sun, 03 Aug 2025 14:32:05 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 17:58:36 +0000</pubDate>
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<title>Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents</title>
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<title>I Think, Therefore I Am Under-Qualified? A Benchmark for Evaluating Linguistic Shibboleth Detection in LLM Hiring Evaluations</title>
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<link>https://arxiv.org/abs/2508.01858</link>
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<link>https://arxiv.org/abs/2508.04939</link>
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||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01858.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuhan Guo, Cong Guo, Aiwen Sun, Hongliang He, Xinyu Yang, Yue Lu, Yingji Zhang, Xuntao Guo, Dong Zhang, Jianzhuang Liu, Jiang Duan, Yijia Xiao, Liangjian Wen, Hai-Ming Xu, Yong Dai</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Multimodal large-scale models have significantly advanced the development of web agents, enabling perception and interaction with digital environments akin to human cognition. In this paper, we argue that web agents must first acquire sufficient knowledge to effectively engage in cognitive reasoning. Therefore, we decompose a web agent's capabilities into two essential stages: knowledge content learning and cognitive processes. To formalize this, we propose Web-CogKnowledge Framework, categorizing knowledge as Factual, Conceptual, and Procedural. In this framework, knowledge content learning corresponds to the agent's processes of Memorizing and Understanding, which rely on the first two knowledge types, representing the "what" of learning. Conversely, cognitive processes correspond to Exploring, grounded in Procedural knowledge, defining the "how" of reasoning and action. To facilitate knowledge acquisition, we construct the Web-CogDataset, a structured resource curated from 14 real-world websites, designed to systematically instill core knowledge necessary for web agent. This dataset serves as the agent's conceptual grounding-the "nouns" upon which comprehension is built-as well as the basis for learning how to reason and act. Building on this foundation, we operationalize these processes through a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework, developing and training our proposed agent, the Web-CogReasoner. Extensive experimentation reveals its significant superiority over existing models, especially in generalizing to unseen tasks where structured knowledge is decisive. To enable rigorous evaluation, we introduce the Web-CogBench, a comprehensive evaluation suite designed to assess and compare agent performance across the delineated knowledge domains and cognitive capabilities. Our code and data is open sourced at https://github.com/Gnonymous/Web-CogReasoner</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04939.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Julia Kharchenko, Tanya Roosta, Aman Chadha, Chirag Shah</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> This paper introduces a comprehensive benchmark for evaluating how Large Language Models (LLMs) respond to linguistic shibboleths: subtle linguistic markers that can inadvertently reveal demographic attributes such as gender, social class, or regional background. Through carefully constructed interview simulations using 100 validated question-response pairs, we demonstrate how LLMs systematically penalize certain linguistic patterns, particularly hedging language, despite equivalent content quality. Our benchmark generates controlled linguistic variations that isolate specific phenomena while maintaining semantic equivalence, which enables the precise measurement of demographic bias in automated evaluation systems. We validate our approach along multiple linguistic dimensions, showing that hedged responses receive 25.6% lower ratings on average, and demonstrate the benchmark's effectiveness in identifying model-specific biases. This work establishes a foundational framework for detecting and measuring linguistic discrimination in AI systems, with broad applications to fairness in automated decision-making contexts.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01858</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04939</guid>
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||||||
<pubDate>Sun, 03 Aug 2025 17:17:52 +0000</pubDate>
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<pubDate>Wed, 06 Aug 2025 23:51:03 +0000</pubDate>
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<title>IAUNet: Instance-Aware U-Net</title>
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<title>REINA: Regularized Entropy Information-Based Loss for Efficient Simultaneous Speech Translation</title>
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||||||
<link>https://arxiv.org/abs/2508.01928</link>
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<link>https://arxiv.org/abs/2508.04946</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01928.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yaroslav Prytula, Illia Tsiporenko, Ali Zeynalli, Dmytro Fishman</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Instance segmentation is critical in biomedical imaging to accurately distinguish individual objects like cells, which often overlap and vary in size. Recent query-based methods, where object queries guide segmentation, have shown strong performance. While U-Net has been a go-to architecture in medical image segmentation, its potential in query-based approaches remains largely unexplored. In this work, we present IAUNet, a novel query-based U-Net architecture. The core design features a full U-Net architecture, enhanced by a novel lightweight convolutional Pixel decoder, making the model more efficient and reducing the number of parameters. Additionally, we propose a Transformer decoder that refines object-specific features across multiple scales. Finally, we introduce the 2025 Revvity Full Cell Segmentation Dataset, a unique resource with detailed annotations of overlapping cell cytoplasm in brightfield images, setting a new benchmark for biomedical instance segmentation. Experiments on multiple public datasets and our own show that IAUNet outperforms most state-of-the-art fully convolutional, transformer-based, and query-based models and cell segmentation-specific models, setting a strong baseline for cell instance segmentation tasks. Code is available at https://github.com/SlavkoPrytula/IAUNet</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04946.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Nameer Hirschkind, Joseph Liu, Mahesh Kumar Nandwana, Xiao Yu</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Simultaneous Speech Translation (SimulST) systems stream in audio while simultaneously emitting translated text or speech. Such systems face the significant challenge of balancing translation quality and latency. We introduce a strategy to optimize this tradeoff: wait for more input only if you gain information by doing so. Based on this strategy, we present Regularized Entropy INformation Adaptation (REINA), a novel loss to train an adaptive policy using an existing non-streaming translation model. We derive REINA from information theory principles and show that REINA helps push the reported Pareto frontier of the latency/quality tradeoff over prior works. Utilizing REINA, we train a SimulST model on French, Spanish and German, both from and into English. Training on only open source or synthetically generated data, we achieve state-of-the-art (SOTA) streaming results for models of comparable size. We also introduce a metric for streaming efficiency, quantitatively showing REINA improves the latency/quality trade-off by as much as 21% compared to prior approaches, normalized against non-streaming baseline BLEU scores.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01928</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04946</guid>
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<pubDate>Sun, 03 Aug 2025 21:36:20 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 00:25:58 +0000</pubDate>
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<title>LeanK: Learnable K Cache Channel Pruning for Efficient Decoding</title>
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<title>Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression</title>
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<link>https://arxiv.org/abs/2508.02215</link>
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<link>https://arxiv.org/abs/2508.04979</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02215.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yike Zhang, Zhiyuan He, Huiqiang Jiang, Chengruidong Zhang, Yuqing Yang, Jianyong Wang, Lili Qiu</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Large language models (LLMs) enable long-context tasks but face efficiency challenges due to the growing key-value (KV) cache. We propose LeanK, a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. With a novel two-stage training process, LeanK learns channel-wise static mask that could satisfy specific sparsity ratio and hardware alignment requirement. LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. Experiments demonstrate up to 70% K cache and 16%-18% V cache memory reduction. Custom decoding kernel enables 1.3x speedup for attention computation. We also provide insights into model channels and attention heads during long-context inference by analyzing the learned importance distribution. Our code is available at https://aka.ms/LeanK.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04979.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zheng Chen, Mingde Zhou, Jinpei Guo, Jiale Yuan, Yifei Ji, Yulun Zhang</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-step sampling, and (2) poor fidelity resulting from over-reliance on generative priors. To address these issues, we propose SODEC, a novel single-step diffusion image compression model. We argue that in image compression, a sufficiently informative latent renders multi-step refinement unnecessary. Based on this insight, we leverage a pre-trained VAE-based model to produce latents with rich information, and replace the iterative denoising process with a single-step decoding. Meanwhile, to improve fidelity, we introduce the fidelity guidance module, encouraging output that is faithful to the original image. Furthermore, we design the rate annealing training strategy to enable effective training under extremely low bitrates. Extensive experiments show that SODEC significantly outperforms existing methods, achieving superior rate-distortion-perception performance. Moreover, compared to previous diffusion-based compression models, SODEC improves decoding speed by more than 20times. Code is released at: https://github.com/zhengchen1999/SODEC.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02215</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04979</guid>
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<pubDate>Mon, 04 Aug 2025 09:08:43 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 02:24:03 +0000</pubDate>
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<title>DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework</title>
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<title>R-Zero: Self-Evolving Reasoning LLM from Zero Data</title>
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<link>https://arxiv.org/abs/2508.02807</link>
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<link>https://arxiv.org/abs/2508.05004</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02807.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tongchun Zuo, Zaiyu Huang, Shuliang Ning, Ente Lin, Chao Liang, Zerong Zheng, Jianwen Jiang, Yuan Zhang, Mingyuan Gao, Xin Dong</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Video virtual try-on (VVT) technology has garnered considerable academic interest owing to its promising applications in e-commerce advertising and entertainment. However, most existing end-to-end methods rely heavily on scarce paired garment-centric datasets and fail to effectively leverage priors of advanced visual models and test-time inputs, making it challenging to accurately preserve fine-grained garment details and maintain temporal consistency in unconstrained scenarios. To address these challenges, we propose DreamVVT, a carefully designed two-stage framework built upon Diffusion Transformers (DiTs), which is inherently capable of leveraging diverse unpaired human-centric data to enhance adaptability in real-world scenarios. To further leverage prior knowledge from pretrained models and test-time inputs, in the first stage, we sample representative frames from the input video and utilize a multi-frame try-on model integrated with a vision-language model (VLM), to synthesize high-fidelity and semantically consistent keyframe try-on images. These images serve as complementary appearance guidance for subsequent video generation. In the second stage, skeleton maps together with fine-grained motion and appearance descriptions are extracted from the input content, and these along with the keyframe try-on images are then fed into a pretrained video generation model enhanced with LoRA adapters. This ensures long-term temporal coherence for unseen regions and enables highly plausible dynamic motions. Extensive quantitative and qualitative experiments demonstrate that DreamVVT surpasses existing methods in preserving detailed garment content and temporal stability in real-world scenarios. Our project page https://virtu-lab.github.io/</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05004.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chengsong Huang, Wenhao Yu, Xiaoyang Wang, Hongming Zhang, Zongxia Li, Ruosen Li, Jiaxin Huang, Haitao Mi, Dong Yu</p><p><b>Upvotes:</b> 63</p><p><b>Summary:</b> Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-tuning or reinforcement learning, which poses a fundamental bottleneck to advancing AI systems toward capabilities beyond human intelligence. To overcome this limitation, we introduce R-Zero, a fully autonomous framework that generates its own training data from scratch. Starting from a single base LLM, R-Zero initializes two independent models with distinct roles, a Challenger and a Solver. These models are optimized separately and co-evolve through interaction: the Challenger is rewarded for proposing tasks near the edge of the Solver capability, and the Solver is rewarded for solving increasingly challenging tasks posed by the Challenger. This process yields a targeted, self-improving curriculum without any pre-existing tasks and labels. Empirically, R-Zero substantially improves reasoning capability across different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02807</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05004</guid>
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<pubDate>Mon, 04 Aug 2025 18:27:55 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 03:38:16 +0000</pubDate>
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<title>CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction</title>
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<title>Attention Basin: Why Contextual Position Matters in Large Language Models</title>
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<link>https://arxiv.org/abs/2508.03159</link>
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<link>https://arxiv.org/abs/2508.05128</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03159.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jueon Park, Yein Park, Minju Song, Soyon Park, Donghyeon Lee, Seungheun Baek, Jaewoo Kang</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> Drug toxicity remains a major challenge in pharmaceutical development. Recent machine learning models have improved in silico toxicity prediction, but their reliance on annotated data and lack of interpretability limit their applicability. This limits their ability to capture organ-specific toxicities driven by complex biological mechanisms. Large language models (LLMs) offer a promising alternative through step-by-step reasoning and integration of textual data, yet prior approaches lack biological context and transparent rationale. To address this issue, we propose CoTox, a novel framework that integrates LLM with chain-of-thought (CoT) reasoning for multi-toxicity prediction. CoTox combines chemical structure data, biological pathways, and gene ontology (GO) terms to generate interpretable toxicity predictions through step-by-step reasoning. Using GPT-4o, we show that CoTox outperforms both traditional machine learning and deep learning model. We further examine its performance across various LLMs to identify where CoTox is most effective. Additionally, we find that representing chemical structures with IUPAC names, which are easier for LLMs to understand than SMILES, enhances the model's reasoning ability and improves predictive performance. To demonstrate its practical utility in drug development, we simulate the treatment of relevant cell types with drug and incorporated the resulting biological context into the CoTox framework. This approach allow CoTox to generate toxicity predictions aligned with physiological responses, as shown in case study. This result highlights the potential of LLM-based frameworks to improve interpretability and support early-stage drug safety assessment. The code and prompt used in this work are available at https://github.com/dmis-lab/CoTox.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05128.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zihao Yi, Delong Zeng, Zhenqing Ling, Haohao Luo, Zhe Xu, Wei Liu, Jian Luan, Wanxia Cao, Ying Shen</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> The performance of Large Language Models (LLMs) is significantly sensitive to the contextual position of information in the input. To investigate the mechanism behind this positional bias, our extensive experiments reveal a consistent phenomenon we term the attention basin: when presented with a sequence of structured items (e.g., retrieved documents or few-shot examples), models systematically assign higher attention to the items at the beginning and end of the sequence, while neglecting those in the middle. Crucially, our analysis further reveals that allocating higher attention to critical information is key to enhancing model performance. Based on these insights, we introduce Attention-Driven Reranking (AttnRank), a two-stage framework that (i) estimates a model's intrinsic positional attention preferences using a small calibration set, and (ii) reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. AttnRank is a model-agnostic, training-free, and plug-and-play method with minimal computational overhead. Experiments on multi-hop QA and few-shot in-context learning tasks demonstrate that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03159</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05128</guid>
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<pubDate>Tue, 05 Aug 2025 07:04:44 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 08:08:08 +0000</pubDate>
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<title>Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following</title>
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<title>DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning</title>
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||||||
<link>https://arxiv.org/abs/2508.03178</link>
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<link>https://arxiv.org/abs/2508.05405</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03178.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chenyang Wang, Liang Wen, Shousheng Jia, Xiangzheng Zhang, Liang Xu</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> While advancements in the reasoning abilities of LLMs have significantly enhanced their performance in solving mathematical problems, coding tasks, and general puzzles, their effectiveness in accurately adhering to instructions remains inconsistent, particularly with more complex directives. Our investigation identifies lazy reasoning during the thinking stage as the primary factor contributing to poor instruction adherence. To mitigate this issue, we propose a comprehensive framework designed to enable rigorous reasoning processes involving preview and self-checking, essential for satisfying strict instruction constraints. Specifically, we first generate instructions with complex constraints and apply a filtering process to obtain valid prompts, resulting in three distinct prompt datasets categorized as hard, easy, and pass. Then, we employ rejection sampling on the pass prompts to curate a small yet high-quality dataset, enabling a cold-start initialization of the model and facilitating its adaptation to effective reasoning patterns. Subsequently, we employ an entropy-preserving supervised fine-tuning (Entropy-SFT) strategy coupled with token-wise entropy-adaptive (TEA-RL) reinforcement learning guided by rule-based dense rewards. This approach encourages the model to transform its reasoning mechanism, ultimately fostering generalizable reasoning abilities that encompass preview and self-checking. Extensive experiments conducted on instruction-following benchmarks demonstrate remarkable performance improvements across various model scales. Notably, our Light-IF-32B model surpasses both larger open-source models such as DeepSeek-R1 and closed-source models like Doubao-1.6.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05405.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xinrun Xu, Pi Bu, Ye Wang, Börje F. Karlsson, Ziming Wang, Tengtao Song, Qi Zhu, Jun Song, Zhiming Ding, Bo Zheng</p><p><b>Upvotes:</b> 51</p><p><b>Summary:</b> Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in complex, dynamic environments, leading to subpar performance. Real-world tasks typically require complex interactions, advanced spatial reasoning, long-term planning, and continuous strategy refinement, usually necessitating understanding the physics rules of the target scenario. However, evaluating these capabilities in real-world scenarios is often prohibitively expensive. To bridge this gap, we introduce DeepPHY, a novel benchmark framework designed to systematically evaluate VLMs' understanding and reasoning about fundamental physical principles through a series of challenging simulated environments. DeepPHY integrates multiple physical reasoning environments of varying difficulty levels and incorporates fine-grained evaluation metrics. Our evaluation finds that even state-of-the-art VLMs struggle to translate descriptive physical knowledge into precise, predictive control.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03178</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05405</guid>
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<pubDate>Tue, 05 Aug 2025 07:42:00 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 13:58:19 +0000</pubDate>
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<title>SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering</title>
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<title>InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities</title>
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||||||
<link>https://arxiv.org/abs/2508.03448</link>
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<link>https://arxiv.org/abs/2508.05496</link>
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||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03448.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jan Melechovsky, Ambuj Mehrish, Dorien Herremans</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster's enhanced outputs over the original degraded audio, highlighting the effectiveness of our unified approach.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05496.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuo Cai, Su Lu, Qi Zhou, Kejing Yang, Zhijie Sang, Congkai Xie, Hongxia Yang</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Large language models (LLMs) have exhibited impressive reasoning abilities on a wide range of complex tasks. However, enhancing these capabilities through post-training remains resource intensive, particularly in terms of data and computational cost. Although recent efforts have sought to improve sample efficiency through selective data curation, existing methods often rely on heuristic or task-specific strategies that hinder scalability. In this work, we introduce InfiAlign, a scalable and sample-efficient post-training framework that integrates supervised fine-tuning (SFT) with Direct Preference Optimization (DPO) to align LLMs for enhanced reasoning. At the core of InfiAlign is a robust data selection pipeline that automatically curates high-quality alignment data from open-source reasoning datasets using multidimensional quality metrics. This pipeline enables significant performance gains while drastically reducing data requirements and remains extensible to new data sources. When applied to the Qwen2.5-Math-7B-Base model, our SFT model achieves performance on par with DeepSeek-R1-Distill-Qwen-7B, while using only approximately 12% of the training data, and demonstrates strong generalization across diverse reasoning tasks. Additional improvements are obtained through the application of DPO, with particularly notable gains in mathematical reasoning tasks. The model achieves an average improvement of 3.89% on AIME 24/25 benchmarks. Our results highlight the effectiveness of combining principled data selection with full-stage post-training, offering a practical solution for aligning large reasoning models in a scalable and data-efficient manner. The model checkpoints are available at https://huggingface.co/InfiX-ai/InfiAlign-Qwen-7B-SFT.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03448</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05496</guid>
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<pubDate>Tue, 05 Aug 2025 13:49:04 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 15:34:06 +0000</pubDate>
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<title>Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning</title>
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<title>PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction</title>
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||||||
<link>https://arxiv.org/abs/2508.03501</link>
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<link>https://arxiv.org/abs/2508.05545</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03501.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Alexander Golubev, Maria Trofimova, Sergei Polezhaev, Ibragim Badertdinov, Maksim Nekrashevich, Anton Shevtsov, Simon Karasik, Sergey Abramov, Andrei Andriushchenko, Filipp Fisin, Sergei Skvortsov, Boris Yangel</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Research on applications of Reinforcement Learning (RL) to Large Language Models (LLMs) has mostly been focused on single-turn problems, such as mathematical reasoning or single-shot code generation. While these problems can be viewed as token-level multi-turn MDPs, this view corresponds to a degenerate case of multi-turn interaction where the environment provides no feedback. This contrasts with many real-world domains, such as software engineering (SWE), which require rich multi-turn interactions with a stateful environment that responds to each action with a non-trivial observation. To bridge this gap, we demonstrate the successful application of RL to this general regime. Using a modified Decoupled Advantage Policy Optimization (DAPO) algorithm, we train an agent based on Qwen2.5-72B-Instruct to solve real-world software engineering tasks. Our approach increases the agent's success rate on the SWE-bench Verified benchmark from a 20% rejection fine-tuned baseline to 39%, without relying on any teacher models. On SWE-rebench, our agent matches or outperforms leading open-weight models such as DeepSeek-V3-0324 and Qwen3-235B-A22B using an identical scaffolding, offering a viable path toward building more capable autonomous agents for complex real-world problems based on open models.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05545.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Leon Garza, Anantaa Kotal, Aritran Piplai, Lavanya Elluri, Prajit Das, Aman Chadha</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Redacting Personally Identifiable Information (PII) from unstructured text is critical for ensuring data privacy in regulated domains. While earlier approaches have relied on rule-based systems and domain-specific Named Entity Recognition (NER) models, these methods fail to generalize across formats and contexts. Recent advances in Large Language Models (LLMs) offer a promising alternative, yet the effect of architectural and training choices on redaction performance remains underexplored. LLMs have demonstrated strong performance in tasks that require contextual language understanding, including the redaction of PII in free-form text. Prior work suggests that with appropriate adaptation, LLMs can become effective contextual privacy learners. However, the consequences of architectural and training choices for PII Redaction remain underexplored. In this work, we present a comprehensive analysis of LLMs as privacy-preserving PII Redaction systems. We evaluate a range of LLM architectures and training strategies for their effectiveness in PII Redaction. Our analysis measures redaction performance, semantic preservation, and PII leakage, and compares these outcomes against latency and computational cost. The results provide practical guidance for configuring LLM-based redactors that are accurate, efficient, and privacy-aware. To support reproducibility and real-world deployment, we release PRvL, an open-source suite of fine-tuned models, and evaluation tools for general-purpose PII Redaction. PRvL is built entirely on open-source LLMs and supports multiple inference settings for flexibility and compliance. It is designed to be easily customized for different domains and fully operable within secure, self-managed environments. This enables data owners to perform redactions without relying on third-party services or exposing sensitive content beyond their own infrastructure.</p></description>
|
||||||
<guid isPermaLink="false">https://arxiv.org/abs/2508.03501</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05545</guid>
|
||||||
<pubDate>Tue, 05 Aug 2025 14:30:47 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 16:22:49 +0000</pubDate>
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<title>LaTCoder: Converting Webpage Design to Code with Layout-as-Thought</title>
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<title>Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity</title>
|
||||||
<link>https://arxiv.org/abs/2508.03560</link>
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<link>https://arxiv.org/abs/2508.05609</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03560.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yi Gui, Zhen Li, Zhongyi Zhang, Guohao Wang, Tianpeng Lv, Gaoyang Jiang, Yi Liu, Dongping Chen, Yao Wan, Hongyu Zhang, Wenbin Jiang, Xuanhua Shi, Hai Jin</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Converting webpage designs into code (design-to-code) plays a vital role in User Interface (UI) development for front-end developers, bridging the gap between visual design and functional implementation. While recent Multimodal Large Language Models (MLLMs) have shown significant potential in design-to-code tasks, they often fail to accurately preserve the layout during code generation. To this end, we draw inspiration from the Chain-of-Thought (CoT) reasoning in human cognition and propose LaTCoder, a novel approach that enhances layout preservation in webpage design during code generation with Layout-as-Thought (LaT). Specifically, we first introduce a simple yet efficient algorithm to divide the webpage design into image blocks. Next, we prompt MLLMs using a CoTbased approach to generate code for each block. Finally, we apply two assembly strategies-absolute positioning and an MLLM-based method-followed by dynamic selection to determine the optimal output. We evaluate the effectiveness of LaTCoder using multiple backbone MLLMs (i.e., DeepSeek-VL2, Gemini, and GPT-4o) on both a public benchmark and a newly introduced, more challenging benchmark (CC-HARD) that features complex layouts. The experimental results on automatic metrics demonstrate significant improvements. Specifically, TreeBLEU scores increased by 66.67% and MAE decreased by 38% when using DeepSeek-VL2, compared to direct prompting. Moreover, the human preference evaluation results indicate that annotators favor the webpages generated by LaTCoder in over 60% of cases, providing strong evidence of the effectiveness of our method.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05609.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuhan Zhang, Long Zhuo, Ziyang Chu, Tong Wu, Zhibing Li, Liang Pan, Dahua Lin, Ziwei Liu</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> Despite rapid advances in 3D content generation, quality assessment for the generated 3D assets remains challenging. Existing methods mainly rely on image-based metrics and operate solely at the object level, limiting their ability to capture spatial coherence, material authenticity, and high-fidelity local details. 1) To address these challenges, we introduce Hi3DEval, a hierarchical evaluation framework tailored for 3D generative content. It combines both object-level and part-level evaluation, enabling holistic assessments across multiple dimensions as well as fine-grained quality analysis. Additionally, we extend texture evaluation beyond aesthetic appearance by explicitly assessing material realism, focusing on attributes such as albedo, saturation, and metallicness. 2) To support this framework, we construct Hi3DBench, a large-scale dataset comprising diverse 3D assets and high-quality annotations, accompanied by a reliable multi-agent annotation pipeline. We further propose a 3D-aware automated scoring system based on hybrid 3D representations. Specifically, we leverage video-based representations for object-level and material-subject evaluations to enhance modeling of spatio-temporal consistency and employ pretrained 3D features for part-level perception. Extensive experiments demonstrate that our approach outperforms existing image-based metrics in modeling 3D characteristics and achieves superior alignment with human preference, providing a scalable alternative to manual evaluations. The project page is available at https://zyh482.github.io/Hi3DEval/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03560</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05609</guid>
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<pubDate>Tue, 05 Aug 2025 15:28:48 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 17:50:13 +0000</pubDate>
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<title>HPSv3: Towards Wide-Spectrum Human Preference Score</title>
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<title>Learning to Reason for Factuality</title>
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<link>https://arxiv.org/abs/2508.03789</link>
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<link>https://arxiv.org/abs/2508.05618</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03789.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuhang Ma, Xiaoshi Wu, Keqiang Sun, Hongsheng Li</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> Evaluating text-to-image generation models requires alignment with human perception, yet existing human-centric metrics are constrained by limited data coverage, suboptimal feature extraction, and inefficient loss functions. To address these challenges, we introduce Human Preference Score v3 (HPSv3). (1) We release HPDv3, the first wide-spectrum human preference dataset integrating 1.08M text-image pairs and 1.17M annotated pairwise comparisons from state-of-the-art generative models and low to high-quality real-world images. (2) We introduce a VLM-based preference model trained using an uncertainty-aware ranking loss for fine-grained ranking. Besides, we propose Chain-of-Human-Preference (CoHP), an iterative image refinement method that enhances quality without extra data, using HPSv3 to select the best image at each step. Extensive experiments demonstrate that HPSv3 serves as a robust metric for wide-spectrum image evaluation, and CoHP offers an efficient and human-aligned approach to improve image generation quality. The code and dataset are available at the HPSv3 Homepage.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05618.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xilun Chen, Ilia Kulikov, Vincent-Pierre Berges, Barlas Oğuz, Rulin Shao, Gargi Ghosh, Jason Weston, Wen-tau Yih</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Reasoning Large Language Models (R-LLMs) have significantly advanced complex reasoning tasks but often struggle with factuality, generating substantially more hallucinations than their non-reasoning counterparts on long-form factuality benchmarks. However, extending online Reinforcement Learning (RL), a key component in recent R-LLM advancements, to the long-form factuality setting poses several unique challenges due to the lack of reliable verification methods. Previous work has utilized automatic factuality evaluation frameworks such as FActScore to curate preference data in the offline RL setting, yet we find that directly leveraging such methods as the reward in online RL leads to reward hacking in multiple ways, such as producing less detailed or relevant responses. We propose a novel reward function that simultaneously considers the factual precision, response detail level, and answer relevance, and applies online RL to learn high quality factual reasoning. Evaluated on six long-form factuality benchmarks, our factual reasoning model achieves an average reduction of 23.1 percentage points in hallucination rate, a 23% increase in answer detail level, and no degradation in the overall response helpfulness.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03789</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05618</guid>
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<pubDate>Tue, 05 Aug 2025 17:17:13 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 17:57:09 +0000</pubDate>
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<title>Agent Lightning: Train ANY AI Agents with Reinforcement Learning</title>
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<title>On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification</title>
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<link>https://arxiv.org/abs/2508.03680</link>
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<link>https://arxiv.org/abs/2508.05629</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03680.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xufang Luo, Yuge Zhang, Zhiyuan He, Zilong Wang, Siyun Zhao, Dongsheng Li, Luna K. Qiu, Yuqing Yang</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike existing methods that tightly couple RL training with agent or rely on sequence concatenation with masking, Agent Lightning achieves complete decoupling between agent execution and training, allowing seamless integration with existing agents developed via diverse ways (e.g., using frameworks like LangChain, OpenAI Agents SDK, AutoGen, and building from scratch) with almost ZERO code modifications. By formulating agent execution as Markov decision process, we define an unified data interface and propose a hierarchical RL algorithm, LightningRL, which contains a credit assignment module, allowing us to decompose trajectories generated by ANY agents into training transition. This enables RL to handle complex interaction logic, such as multi-agent scenarios and dynamic workflows. For the system design, we introduce a Training-Agent Disaggregation architecture, and brings agent observability frameworks into agent runtime, providing a standardized agent finetuning interface. Experiments across text-to-SQL, retrieval-augmented generation, and math tool-use tasks demonstrate stable, continuous improvements, showcasing the framework's potential for real-world agent training and deployment.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05629.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yongliang Wu, Yizhou Zhou, Zhou Ziheng, Yingzhe Peng, Xinyu Ye, Xinting Hu, Wenbo Zhu, Lu Qi, Ming-Hsuan Yang, Xu Yang</p><p><b>Upvotes:</b> 77</p><p><b>Summary:</b> We present a simple yet theoretically motivated improvement to Supervised Fine-Tuning (SFT) for the Large Language Model (LLM), addressing its limited generalization compared to reinforcement learning (RL). Through mathematical analysis, we reveal that standard SFT gradients implicitly encode a problematic reward structure that may severely restrict the generalization capabilities of model. To rectify this, we propose Dynamic Fine-Tuning (DFT), stabilizing gradient updates for each token by dynamically rescaling the objective function with the probability of this token. Remarkably, this single-line code change significantly outperforms standard SFT across multiple challenging benchmarks and base models, demonstrating greatly improved generalization. Additionally, our approach shows competitive results in offline RL settings, offering an effective yet simpler alternative. This work bridges theoretical insight and practical solutions, substantially advancing SFT performance. The code will be available at https://github.com/yongliang-wu/DFT.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03680</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05629</guid>
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<pubDate>Tue, 05 Aug 2025 17:50:13 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 17:59:04 +0000</pubDate>
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<title>Sotopia-RL: Reward Design for Social Intelligence</title>
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<title>MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes</title>
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<link>https://arxiv.org/abs/2508.03905</link>
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<link>https://arxiv.org/abs/2508.05630</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03905.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haofei Yu, Zhengyang Qi, Yining Zhao, Kolby Nottingham, Keyang Xuan, Bodhisattwa Prasad Majumder, Hao Zhu, Paul Pu Liang, Jiaxuan You</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> Social intelligence has become a critical capability for large language models (LLMs), enabling them to engage effectively in real-world social tasks such as accommodation, persuasion, collaboration, and negotiation. Reinforcement learning (RL) is a natural fit for training socially intelligent agents because it allows models to learn sophisticated strategies directly through social interactions. However, social interactions have two key characteristics that set barriers for RL training: (1) partial observability, where utterances have indirect and delayed effects that complicate credit assignment, and (2) multi-dimensionality, where behaviors such as rapport-building or knowledge-seeking contribute indirectly to goal achievement. These characteristics make Markov decision process (MDP)-based RL with single-dimensional episode-level rewards inefficient and unstable. To address these challenges, we propose Sotopia-RL, a novel framework that refines coarse episode-level feedback into utterance-level, multi-dimensional rewards. Utterance-level credit assignment mitigates partial observability by attributing outcomes to individual utterances, while multi-dimensional rewards capture the full richness of social interactions and reduce reward hacking. Experiments in Sotopia, an open-ended social learning environment, demonstrate that Sotopia-RL achieves state-of-the-art social goal completion scores (7.17 on Sotopia-hard and 8.31 on Sotopia-full), significantly outperforming existing approaches. Ablation studies confirm the necessity of both utterance-level credit assignment and multi-dimensional reward design for RL training. Our implementation is publicly available at: https://github.com/sotopia-lab/sotopia-rl.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05630.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Henghui Ding, Kaining Ying, Chang Liu, Shuting He, Xudong Jiang, Yu-Gang Jiang, Philip H. S. Torr, Song Bai</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Video object segmentation (VOS) aims to segment specified target objects throughout a video. Although state-of-the-art methods have achieved impressive performance (e.g., 90+% J&F) on existing benchmarks such as DAVIS and YouTube-VOS, these datasets primarily contain salient, dominant, and isolated objects, limiting their generalization to real-world scenarios. To advance VOS toward more realistic environments, coMplex video Object SEgmentation (MOSEv1) was introduced to facilitate VOS research in complex scenes. Building on the strengths and limitations of MOSEv1, we present MOSEv2, a significantly more challenging dataset designed to further advance VOS methods under real-world conditions. MOSEv2 consists of 5,024 videos and over 701,976 high-quality masks for 10,074 objects across 200 categories. Compared to its predecessor, MOSEv2 introduces significantly greater scene complexity, including more frequent object disappearance and reappearance, severe occlusions and crowding, smaller objects, as well as a range of new challenges such as adverse weather (e.g., rain, snow, fog), low-light scenes (e.g., nighttime, underwater), multi-shot sequences, camouflaged objects, non-physical targets (e.g., shadows, reflections), scenarios requiring external knowledge, etc. We benchmark 20 representative VOS methods under 5 different settings and observe consistent performance drops. For example, SAM2 drops from 76.4% on MOSEv1 to only 50.9% on MOSEv2. We further evaluate 9 video object tracking methods and find similar declines, demonstrating that MOSEv2 presents challenges across tasks. These results highlight that despite high accuracy on existing datasets, current VOS methods still struggle under real-world complexities. MOSEv2 is publicly available at https://MOSE.video.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03905</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05630</guid>
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||||||
<pubDate>Tue, 05 Aug 2025 20:43:42 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 17:59:27 +0000</pubDate>
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<title>Data and AI governance: Promoting equity, ethics, and fairness in large language models</title>
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<title>Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation</title>
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<link>https://arxiv.org/abs/2508.03970</link>
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<link>https://arxiv.org/abs/2508.05635</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03970.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Alok Abhishek, Lisa Erickson, Tushar Bandopadhyay</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. Building upon our foundational work on the Bias Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the authors share prevalent bias and fairness related gaps in Large Language Models (LLMs) and discuss data and AI governance framework to address Bias, Ethics, Fairness, and Factuality within LLMs. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Ultimately, through this article, we aim to contribute to advancement of the creation and deployment of socially responsible and ethically aligned generative artificial intelligence powered applications.</p></description>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.05635.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, Liliang Chen, Shuicheng Yan, Maoqing Yao, Guanghui Ren</p><p><b>Upvotes:</b> 54</p><p><b>Summary:</b> We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation that integrates policy learning, evaluation, and simulation within a single video-generative framework. At its core, GE-Base is a large-scale, instruction-conditioned video diffusion model that captures the spatial, temporal, and semantic dynamics of real-world robotic interactions in a structured latent space. Built upon this foundation, GE-Act maps latent representations to executable action trajectories through a lightweight, flow-matching decoder, enabling precise and generalizable policy inference across diverse embodiments with minimal supervision. To support scalable evaluation and training, GE-Sim serves as an action-conditioned neural simulator, producing high-fidelity rollouts for closed-loop policy development. The platform is further equipped with EWMBench, a standardized benchmark suite measuring visual fidelity, physical consistency, and instruction-action alignment. Together, these components establish Genie Envisioner as a scalable and practical foundation for instruction-driven, general-purpose embodied intelligence. All code, models, and benchmarks will be released publicly.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03970</guid>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.05635</guid>
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||||||
<pubDate>Tue, 05 Aug 2025 23:15:31 +0000</pubDate>
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<pubDate>Thu, 07 Aug 2025 17:59:44 +0000</pubDate>
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<title>MiDashengLM: Efficient Audio Understanding with General Audio Captions</title>
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<link>https://arxiv.org/abs/2508.03983</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.03983.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Heinrich Dinkel, Gang Li, Jizhong Liu, Jian Luan, Yadong Niu, Xingwei Sun, Tianzi Wang, Qiyang Xiao, Junbo Zhang, Jiahao Zhou</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Current approaches for large audio language models (LALMs) often rely on closed data sources or proprietary models, limiting their generalization and accessibility. This paper introduces MiDashengLM, a novel open audio-language model designed for efficient and comprehensive audio understanding through the use of general audio captions using our novel ACAVCaps training dataset. MiDashengLM exclusively relies on publicly available pretraining and supervised fine-tuning (SFT) datasets, ensuring full transparency and reproducibility. At its core, MiDashengLM integrates Dasheng, an open-source audio encoder, specifically engineered to process diverse auditory information effectively. Unlike previous works primarily focused on Automatic Speech Recognition (ASR) based audio-text alignment, our strategy centers on general audio captions, fusing speech, sound and music information into one textual representation, enabling a holistic textual representation of complex audio scenes. Lastly, MiDashengLM provides an up to 4x speedup in terms of time-to-first-token (TTFT) and up to 20x higher throughput than comparable models. Checkpoints are available online at https://huggingface.co/mispeech/midashenglm-7b and https://github.com/xiaomi-research/dasheng-lm.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.03983</guid>
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||||||
<pubDate>Wed, 06 Aug 2025 00:30:19 +0000</pubDate>
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<title>HarmonyGuard: Toward Safety and Utility in Web Agents via Adaptive Policy Enhancement and Dual-Objective Optimization</title>
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|
||||||
<link>https://arxiv.org/abs/2508.04010</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04010.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yurun Chen, Xavier Hu, Yuhan Liu, Keting Yin, Juncheng Li, Zhuosheng Zhang, Shengyu Zhang</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Large language models enable agents to autonomously perform tasks in open web environments. However, as hidden threats within the web evolve, web agents face the challenge of balancing task performance with emerging risks during long-sequence operations. Although this challenge is critical, current research remains limited to single-objective optimization or single-turn scenarios, lacking the capability for collaborative optimization of both safety and utility in web environments. To address this gap, we propose HarmonyGuard, a multi-agent collaborative framework that leverages policy enhancement and objective optimization to jointly improve both utility and safety. HarmonyGuard features a multi-agent architecture characterized by two fundamental capabilities: (1) Adaptive Policy Enhancement: We introduce the Policy Agent within HarmonyGuard, which automatically extracts and maintains structured security policies from unstructured external documents, while continuously updating policies in response to evolving threats. (2) Dual-Objective Optimization: Based on the dual objectives of safety and utility, the Utility Agent integrated within HarmonyGuard performs the Markovian real-time reasoning to evaluate the objectives and utilizes metacognitive capabilities for their optimization. Extensive evaluations on multiple benchmarks show that HarmonyGuard improves policy compliance by up to 38% and task completion by up to 20% over existing baselines, while achieving over 90% policy compliance across all tasks. Our project is available here: https://github.com/YurunChen/HarmonyGuard.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04010</guid>
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<pubDate>Wed, 06 Aug 2025 01:49:32 +0000</pubDate>
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<title>VeriGUI: Verifiable Long-Chain GUI Dataset</title>
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<link>https://arxiv.org/abs/2508.04026</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04026.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shunyu Liu, Minghao Liu, Huichi Zhou, Zhenyu Cui, Yang Zhou, Yuhao Zhou, Wendong Fan, Ge Zhang, Jiajun Shi, Weihao Xuan, Jiaxing Huang, Shuang Luo, Fang Wu, Heli Qi, Qingcheng Zeng, Ziqi Ren, Jialiang Gao, Jindi Lv, Junjie Wang, Aosong Feng, Heng Zhou, Wangchunshu Zhou, Zhenfei Yin, Wenlong Zhang, Guohao Li, Wenhao Yu, Irene Li, Lei Ma, Lei Bai, Qunshu Lin, Mingli Song, Dacheng Tao</p><p><b>Upvotes:</b> 116</p><p><b>Summary:</b> Recent studies have delved into constructing autonomous agents capable of performing complex Graphical User Interface (GUI)-based computer tasks, with the potential to revolutionize human-computer interaction. Despite encouraging results, existing efforts mainly focus on short-term interactions and rely on outcome-only verification, thereby limiting their scalability in real-world GUI applications that demand long-horizon task decomposition and execution. In this work, we introduce VeriGUI, a novel verifiable long-chain GUI dataset designed to facilitate the development and evaluation of generalist GUI agents operating in realistic computer environments. Our dataset emphasizes two critical dimensions: (1) long-chain complexity, with tasks decomposed into a sequence of interdependent subtasks spanning hundreds of steps, explicitly designed to allow any subtask to serve as a valid starting point; and (2) subtask-level verifiability, which enables diverse exploration strategies within each subtask, while ensuring that each subtask-level goal remains verifiable and consistent. The dataset consists of GUI task trajectories across both desktop and web, annotated by human experts. Extensive experiments on VeriGUI using various agents with different foundation models reveal significant performance gaps in handling long-horizon tasks, highlighting the need for more robust planning and decision-making capabilities in GUI agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04026</guid>
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||||||
<pubDate>Wed, 06 Aug 2025 02:38:18 +0000</pubDate>
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<title>Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success</title>
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||||||
<link>https://arxiv.org/abs/2508.04280</link>
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|
||||||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04280.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> George Bredis, Stanislav Dereka, Viacheslav Sinii, Ruslan Rakhimov, Daniil Gavrilov</p><p><b>Upvotes:</b> 26</p><p><b>Summary:</b> Interactive multimodal agents must convert raw visual observations into coherent sequences of language-conditioned actions -- a capability that current vision-language models (VLMs) still lack. Earlier reinforcement-learning (RL) efforts could, in principle, endow VLMs with such skills, but they have seldom tested whether the learned behaviours generalize beyond their training simulators, and they depend either on brittle hyperparameter tuning or on dense-reward environments with low state variability. We introduce Vision-Language Decoupled Actor-Critic (VL-DAC), a lightweight, hyperparameter-free RL algorithm. VL-DAC applies PPO updates to action tokens while learning value only at the environment-step level: an arrangement, to our knowledge, not previously explored for large VLMs or LLMs. This simple decoupling removes unstable weighting terms and yields faster, more reliable convergence. Training a single VLM with VL-DAC in one inexpensive simulator at a time (MiniWorld, Gym-Cards, ALFWorld, or WebShop) already produces policies that generalize widely: +50\% relative on BALROG (game-centric agentic control), +5\% relative on the hardest part of VSI-Bench (spatial planning), and +2\% on VisualWebBench (web navigation), all without degrading general image understanding accuracy. These results provide the first evidence that a simple RL algorithm can train VLMs entirely in cheap synthetic worlds while delivering measurable gains on real-image agentic, spatial-reasoning, and web-navigation benchmarks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04280</guid>
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||||||
<pubDate>Wed, 06 Aug 2025 10:08:48 +0000</pubDate>
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||||||
<title>EVOC2RUST: A Skeleton-guided Framework for Project-Level C-to-Rust Translation</title>
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||||||
<link>https://arxiv.org/abs/2508.04295</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04295.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chaofan Wang, Tingrui Yu, Jie Wang, Dong Chen, Wenrui Zhang, Yuling Shi, Xiaodong Gu, Beijun Shen</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Rust's compile-time safety guarantees make it ideal for safety-critical systems, creating demand for translating legacy C codebases to Rust. While various approaches have emerged for this task, they face inherent trade-offs: rule-based solutions face challenges in meeting code safety and idiomaticity requirements, while LLM-based solutions often fail to generate semantically equivalent Rust code, due to the heavy dependencies of modules across the entire codebase. Recent studies have revealed that both solutions are limited to small-scale programs. In this paper, we propose EvoC2Rust, an automated framework for converting entire C projects to equivalent Rust ones. EvoC2Rust employs a skeleton-guided translation strategy for project-level translation. The pipeline consists of three evolutionary stages: 1) it first decomposes the C project into functional modules, employs a feature-mapping-enhanced LLM to transform definitions and macros and generates type-checked function stubs, which form a compilable Rust skeleton; 2) it then incrementally translates the function, replacing the corresponding stub placeholder; 3) finally, it repairs compilation errors by integrating LLM and static analysis. Through evolutionary augmentation, EvoC2Rust combines the advantages of both rule-based and LLM-based solutions. Our evaluation on open-source benchmarks and six industrial projects demonstrates EvoC2Rust's superior performance in project-level C-to-Rust translation. On average, it achieves 17.24% and 14.32% improvements in syntax and semantic accuracy over the LLM-based approaches, along with a 96.79% higher code safety rate than the rule-based tools. At the module level, EvoC2Rust reaches 92.25% compilation and 89.53% test pass rates on industrial projects, even for complex codebases and long functions.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04295</guid>
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<pubDate>Wed, 06 Aug 2025 10:31:23 +0000</pubDate>
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<title>StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion</title>
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<link>https://arxiv.org/abs/2508.04440</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04440.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yutong Wu, Di Huang, Ruosi Wan, Yue Peng, Shijie Shang, Chenrui Cao, Lei Qi, Rui Zhang, Zidong Du, Jie Yan, Xing Hu</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffer from low accuracy. We identify two key abilities for effective autoformalization: comprehensive mastery of formal-language domain knowledge, and reasoning capability of natural language problem understanding and informal-formal alignment. Without the former, a model cannot identify the correct formal objects; without the latter, it struggles to interpret real-world contexts and map them precisely into formal expressions. To address these gaps, we introduce ThinkingF, a data synthesis and training pipeline that improves both abilities. First, we construct two datasets: one by distilling and selecting large-scale examples rich in formal knowledge, and another by generating informal-to-formal reasoning trajectories guided by expert-designed templates. We then apply SFT and RLVR with these datasets to further fuse and refine the two abilities. The resulting 7B and 32B models exhibit both comprehensive formal knowledge and strong informal-to-formal reasoning. Notably, StepFun-Formalizer-32B achieves SOTA BEq@1 scores of 40.5% on FormalMATH-Lite and 26.7% on ProverBench, surpassing all prior general-purpose and specialized models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04440</guid>
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<pubDate>Wed, 06 Aug 2025 13:28:22 +0000</pubDate>
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<title>Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference</title>
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<link>https://arxiv.org/abs/2508.04586</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04586.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Nuo Chen, Moming Duan, Andre Huikai Lin, Qian Wang, Jiaying Wu, Bingsheng He</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04586</guid>
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<pubDate>Wed, 06 Aug 2025 16:08:27 +0000</pubDate>
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<title>IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards</title>
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<link>https://arxiv.org/abs/2508.04632</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04632.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xu Guo, Tianyi Liang, Tong Jian, Xiaogui Yang, Ling-I Wu, Chenhui Li, Zhihui Lu, Qipeng Guo, Kai Chen</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Reinforcement Learning with Verifiable Rewards (RLVR) improves instruction following capabilities of large language models (LLMs), but suffers from training inefficiency due to inadequate difficulty assessment. Moreover, RLVR is prone to over-optimization, where LLMs exploit verification shortcuts without aligning to the actual intent of user instructions. We introduce Instruction Following Decorator (IFDecorator}, a framework that wraps RLVR training into a robust and sample-efficient pipeline. It consists of three components: (1) a cooperative-adversarial data flywheel that co-evolves instructions and hybrid verifications, generating progressively more challenging instruction-verification pairs; (2) IntentCheck, a bypass module enforcing intent alignment; and (3) trip wires, a diagnostic mechanism that detects reward hacking via trap instructions, which trigger and capture shortcut exploitation behaviors. Our Qwen2.5-32B-Instruct-IFDecorator achieves 87.43% accuracy on IFEval, outperforming larger proprietary models such as GPT-4o. Additionally, we demonstrate substantial improvements on FollowBench while preserving general capabilities. Our trip wires show significant reductions in reward hacking rates. We will release models, code, and data for future research.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04632</guid>
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<pubDate>Wed, 06 Aug 2025 17:00:54 +0000</pubDate>
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<title>Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management</title>
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<link>https://arxiv.org/abs/2508.04664</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04664.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mo Li, L. H. Xu, Qitai Tan, Ting Cao, Yunxin Liu</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall. While most research focuses on external memory systems to augment LLMs' capabilities, we propose a complementary approach: empowering LLMs with Active Context Management (ACM) tools to actively sculpt their internal working memory. We introduce Sculptor, a framework that equips LLMs with three categories of tools: (1) context fragmentation, (2) summary, hide, and restore, and (3) intelligent search. Our approach enables LLMs to proactively manage their attention and working memory, analogous to how humans selectively focus on relevant information while filtering out distractions. Experimental evaluation on information-sparse benchmarks-PI-LLM (proactive interference) and NeedleBench Multi-Needle Reasoning-demonstrates that Sculptor significantly improves performance even without specific training, leveraging LLMs' inherent tool calling generalization capabilities. By enabling Active Context Management, Sculptor not only mitigates proactive interference but also provides a cognitive foundation for more reliable reasoning across diverse long-context tasks-highlighting that explicit context-control strategies, rather than merely larger token windows, are key to robustness at scale.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04664</guid>
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<pubDate>Wed, 06 Aug 2025 17:32:58 +0000</pubDate>
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<title>SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience</title>
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<link>https://arxiv.org/abs/2508.04700</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.04700.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zeyi Sun, Ziyu Liu, Yuhang Zang, Yuhang Cao, Xiaoyi Dong, Tong Wu, Dahua Lin, Jiaqi Wang</p><p><b>Upvotes:</b> 37</p><p><b>Summary:</b> Repurposing large vision-language models (LVLMs) as computer use agents (CUAs) has led to substantial breakthroughs, primarily driven by human-labeled data. However, these models often struggle with novel and specialized software, particularly in scenarios lacking human annotations. To address this challenge, we propose SEAgent, an agentic self-evolving framework enabling CUAs to autonomously evolve through interactions with unfamiliar software. Specifically, SEAgent empowers computer-use agents to autonomously master novel software environments via experiential learning, where agents explore new software, learn through iterative trial-and-error, and progressively tackle auto-generated tasks organized from simple to complex. To achieve this goal, we design a World State Model for step-wise trajectory assessment, along with a Curriculum Generator that generates increasingly diverse and challenging tasks. The agent's policy is updated through experiential learning, comprised of adversarial imitation of failure actions and Group Relative Policy Optimization (GRPO) on successful ones. Furthermore, we introduce a specialist-to-generalist training strategy that integrates individual experiential insights from specialist agents, facilitating the development of a stronger generalist CUA capable of continuous autonomous evolution. This unified agent ultimately achieves performance surpassing ensembles of individual specialist agents on their specialized software. We validate the effectiveness of SEAgent across five novel software environments within OS-World. Our approach achieves a significant improvement of 23.2% in success rate, from 11.3% to 34.5%, over a competitive open-source CUA, i.e., UI-TARS.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.04700</guid>
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<pubDate>Wed, 06 Aug 2025 17:58:46 +0000</pubDate>
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Reference in New Issue
Block a user