🔹 Title: Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09131
• PDF: https://arxiv.org/pdf/2508.09131

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🔹 Title: Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

🔹 Publication Date: Published on Aug 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13167
• PDF: https://arxiv.org/pdf/2508.13167

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🔹 Title: OmniTry: Virtual Try-On Anything without Masks

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13632
• PDF: https://arxiv.org/pdf/2508.13632
• Project Page: https://omnitry.github.io/
• Github: https://omnitry.github.io/

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🔹 Title: MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13992
• PDF: https://arxiv.org/pdf/2508.13992

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🔹 Title: Leveraging Large Language Models for Predictive Analysis of Human Misery

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12669
• PDF: https://arxiv.org/pdf/2508.12669

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🔹 Title: SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution

🔹 Publication Date: Published on Jul 31

🔹 Abstract: SWE-Debate, a competitive multi-agent framework, enhances issue resolution in software engineering by promoting diverse reasoning and achieving better issue localization and fix planning. AI-generated summary Issue resolution has made remarkable progress thanks to the advanced reasoning capabilities of large language models (LLMs). Recently, agent-based frameworks such as SWE-agent have further advanced this progress by enabling autonomous, tool-using agents to tackle complex software engineering tasks . While existing agent-based issue resolution approaches are primarily based on agents' independent explorations, they often get stuck in local solutions and fail to identify issue patterns that span across different parts of the codebase . To address this limitation, we propose SWE-Debate, a competitive multi-agent debate framework that encourages diverse reasoning paths and achieves more consolidated issue localization. SWE-Debate first creates multiple fault propagation traces as localization proposals by traversing a code dependency graph . Then, it organizes a three-round debate among specialized agents , each embodying distinct reasoning perspectives along the fault propagation trace. This structured competition enables agents to collaboratively converge on a consolidated fix plan . Finally, this consolidated fix plan is integrated into an MCTS-based code modification agent for patch generation. Experiments on the SWE-bench benchmark show that SWE-Debate achieves new state-of-the-art results in open-source agent frameworks and outperforms baselines by a large margin.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23348

• PDF: https://arxiv.org/pdf/2507.23348

• Github: https://github.com/YerbaPage/SWE-Debate

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🔹 Title: MultiRef: Controllable Image Generation with Multiple Visual References

🔹 Publication Date: Published on Aug 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06905
• PDF: https://arxiv.org/pdf/2508.06905
• Github: https://multiref.github.io/

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🔹 Title: Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13139
• PDF: https://arxiv.org/pdf/2508.13139

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🔹 Title: RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

🔹 Publication Date: Published on Jul 31

🔹 Abstract: RL-PLUS, a hybrid-policy optimization approach, enhances LLM reasoning capabilities by integrating Multiple Importance Sampling and Exploration-Based Advantage Function, outperforming RLVR on various benchmarks and resolving capability boundary collapse. AI-generated summary 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.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.00222

• PDF: https://arxiv.org/pdf/2508.00222

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🔹 Title: TempFlow-GRPO: When Timing Matters for GRPO in Flow Models

🔹 Publication Date: Published on Aug 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04324
• PDF: https://arxiv.org/pdf/2508.04324
• Project Page: https://tempflowgrpo.github.io/
• Github: https://github.com/Shredded-Pork/TempFlow-GRPO

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🔹 Title: Advances in Speech Separation: Techniques, Challenges, and Future Trends

🔹 Publication Date: Published on Aug 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2508.10830
• PDF: https://arxiv.org/pdf/2508.10830
• Project Page: https://cslikai.cn/Speech-Separation-Paper-Tutorial
• Github: https://github.com/JusperLee/Speech-Separation-Paper-Tutorial

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🔹 Title: Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends

🔹 Publication Date: Published on Aug 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.11548
• PDF: https://arxiv.org/pdf/2508.11548
• Github: https://xuzhenhua55.github.io/awesome-llm-copyright-protection/index.html

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🔹 Title: CorrSteer: Steering Improves Task Performance and Safety in LLMs through Correlation-based Sparse Autoencoder Feature Selection

🔹 Publication Date: Published on Aug 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.12535
• PDF: https://arxiv.org/pdf/2508.12535

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🔹 Title: Radiance Fields in XR: A Survey on How Radiance Fields are Envisioned and Addressed for XR Research

🔹 Publication Date: Published on Aug 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04326
• PDF: https://arxiv.org/pdf/2508.04326
• Project Page: https://mediated-reality.github.io/rf4xr/papers/li_tvcg25/
• Github: https://github.com/mediated-reality/awesome-rf4xr

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🔹 Title: ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents

🔹 Publication Date: Published on Aug 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04038
• PDF: https://arxiv.org/pdf/2508.04038
• Github: https://github.com/zechenli03/ZARA

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🔹 Title: Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08777
• PDF: https://arxiv.org/pdf/2508.08777

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🔹 Title: MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation

🔹 Publication Date: Published on Aug 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.11032
• PDF: https://arxiv.org/pdf/2508.11032
• Github: https://github.com/podismine/MedSAMix

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🔹 Title: Semantic IDs for Joint Generative Search and Recommendation

🔹 Publication Date: Published on Aug 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.10478
• PDF: https://arxiv.org/pdf/2508.10478

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🔹 Title: Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations

🔹 Publication Date: Published on Aug 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09789
• PDF: https://arxiv.org/pdf/2508.09789

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/marcodena/video-recs-describe-what-you-see

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🔹 Title: Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13998
• PDF: https://arxiv.org/pdf/2508.13998

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