🔹 Title: Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents

🔹 Publication Date: Published on Aug 3

🔹 Abstract: A framework for web agents decomposes their capabilities into knowledge content learning and cognitive processes, using a structured dataset and a novel reasoning framework to enhance generalization and performance. AI-generated summary 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

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.01858

• PDF: https://arxiv.org/pdf/2508.01858

• Project Page: https://eohan.me/Web-CogReasoner

• Github: https://Gnonymous.github.io/Web-CogReasoner

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• https://huggingface.co/datasets/Gnonymous/Web-CogDataset

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🔹 Title: Beyond Transcription: Mechanistic Interpretability in ASR

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15882
• PDF: https://arxiv.org/pdf/2508.15882

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🔹 Title: Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17924
• PDF: https://arxiv.org/pdf/2508.17924
• Project Page: https://huggingface.co/datasets/kyegorov/mcd_rppg
• Github: https://github.com/ksyegorov/mcd_rppg

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• https://huggingface.co/datasets/kyegorov/mcd_rppg

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🔹 Title: SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18179
• PDF: https://arxiv.org/pdf/2508.18179
• Project Page: https://lilv98.github.io/SEAM-Website/
• Github: https://github.com/CSSLab/SEAM

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/lilvjosephtang/SEAM-Benchmark

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🔹 Title: Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19827
• PDF: https://arxiv.org/pdf/2508.19827

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🔹 Title: Training a Foundation Model for Materials on a Budget

🔹 Publication Date: Published on Aug 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16067
• PDF: https://arxiv.org/pdf/2508.16067
• Github: https://github.com/atomicarchitects/nequix

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🔹 Title: rStar2-Agent: Agentic Reasoning Technical Report

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20722
• PDF: https://arxiv.org/pdf/2508.20722

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/rstar2-reproduce/rStar2-Agent-RL-Data

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🔹 Title: MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20453
• PDF: https://arxiv.org/pdf/2508.20453
• Github: https://github.com/Accenture/mcp-bench

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🔹 Title: Mixture of Contexts for Long Video Generation

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21058
• PDF: https://arxiv.org/pdf/2508.21058
• Project Page: https://primecai.github.io/moc/
• Github: https://primecai.github.io/moc/

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🔹 Title: Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2508.20751
• PDF: https://arxiv.org/pdf/2508.20751
• Project Page: https://codegoat24.github.io/UnifiedReward/Pref-GRPO
• Github: https://github.com/CodeGoat24/UniGenBench

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🔹 Title: ROSE: Remove Objects with Side Effects in Videos

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18633
• PDF: https://arxiv.org/pdf/2508.18633
• Project Page: https://rose2025-inpaint.github.io/
• Github: https://github.com/Kunbyte-AI/ROSE

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🔹 Title: Collaborative Multi-Modal Coding for High-Quality 3D Generation

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15228
• PDF: https://arxiv.org/pdf/2508.15228

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🔹 Title: USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18966
• PDF: https://arxiv.org/pdf/2508.18966
• Project Page: https://bytedance.github.io/USO/
• Github: https://bytedance.github.io/USO/

🔹 Datasets citing this paper:
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🔹 Spaces citing this paper:
• https://huggingface.co/spaces/bytedance-research/USO
• https://huggingface.co/spaces/bep40/USO
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🔹 Title: AWorld: Orchestrating the Training Recipe for Agentic AI

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20404
• PDF: https://arxiv.org/pdf/2508.20404
• Github: https://github.com/inclusionAI/AWorld/tree/main

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🔹 Title: TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20374
• PDF: https://arxiv.org/pdf/2508.20374

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🔹 Title: Dress&Dance: Dress up and Dance as You Like It - Technical Preview

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21070
• PDF: https://arxiv.org/pdf/2508.21070

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🔹 Title: OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21061
• PDF: https://arxiv.org/pdf/2508.21061

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🔹 Title: FakeParts: a New Family of AI-Generated DeepFakes

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21052
• PDF: https://arxiv.org/pdf/2508.21052

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🔹 Title: CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21046
• PDF: https://arxiv.org/pdf/2508.21046
• Github: https://github.com/JiuTian-VL/CogVLA

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🔹 Title: Provable Benefits of In-Tool Learning for Large Language Models

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20755
• PDF: https://arxiv.org/pdf/2508.20755

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