🔹 Title: Explain Before You Answer: A Survey on Compositional Visual Reasoning
🔹 Publication Date: Published on Aug 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17298
• PDF: https://arxiv.org/pdf/2508.17298
• Project Page: https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey
• Github: https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey
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🔹 Publication Date: Published on Aug 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17298
• PDF: https://arxiv.org/pdf/2508.17298
• Project Page: https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey
• Github: https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey
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🔹 Title: Agent Lightning: Train ANY AI Agents with Reinforcement Learning
🔹 Publication Date: Published on Aug 5
🔹 Abstract: Agent Lightning is a flexible RL framework for training LLMs in various agents, using a hierarchical RL algorithm and decoupling execution from training to handle complex interactions. AI-generated summary 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.
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680
• PDF: https://arxiv.org/pdf/2508.03680
• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Github: https://github.com/microsoft/agent-lightning
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🔹 Publication Date: Published on Aug 5
🔹 Abstract: Agent Lightning is a flexible RL framework for training LLMs in various agents, using a hierarchical RL algorithm and decoupling execution from training to handle complex interactions. AI-generated summary 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.
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680
• PDF: https://arxiv.org/pdf/2508.03680
• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Github: https://github.com/microsoft/agent-lightning
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🔹 Title: MV-RAG: Retrieval Augmented Multiview Diffusion
🔹 Publication Date: Published on Aug 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16577
• PDF: https://arxiv.org/pdf/2508.16577
• Project Page: https://yosefdayani.github.io/MV-RAG/
• Github: https://github.com/yosefdayani/MV-RAG
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🔹 Publication Date: Published on Aug 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16577
• PDF: https://arxiv.org/pdf/2508.16577
• Project Page: https://yosefdayani.github.io/MV-RAG/
• Github: https://github.com/yosefdayani/MV-RAG
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🔹 Title: MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment
🔹 Publication Date: Published on Aug 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17290
• PDF: https://arxiv.org/pdf/2508.17290
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🔹 Publication Date: Published on Aug 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17290
• PDF: https://arxiv.org/pdf/2508.17290
🔹 Datasets citing this paper:
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❤1
🔹 Title: German4All - A Dataset and Model for Readability-Controlled Paraphrasing in German
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17973
• PDF: https://arxiv.org/pdf/2508.17973
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17973
• PDF: https://arxiv.org/pdf/2508.17973
🔹 Datasets citing this paper:
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❤1
🔹 Title: Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
🔹 Publication Date: Published on Aug 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16745
• PDF: https://arxiv.org/pdf/2508.16745
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16745
• PDF: https://arxiv.org/pdf/2508.16745
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❤1
🔹 Title: Limitations of Normalization in Attention Mechanism
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17821
• PDF: https://arxiv.org/pdf/2508.17821
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17821
• PDF: https://arxiv.org/pdf/2508.17821
🔹 Datasets citing this paper:
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🔹 Title: MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17811
• PDF: https://arxiv.org/pdf/2508.17811
• Project Page: https://hanzhichang.github.io/meshsplat_web
• Github: https://hanzhichang.github.io/meshsplat_web
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17811
• PDF: https://arxiv.org/pdf/2508.17811
• Project Page: https://hanzhichang.github.io/meshsplat_web
• Github: https://hanzhichang.github.io/meshsplat_web
🔹 Datasets citing this paper:
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❤1
🔹 Title: REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework
🔹 Publication Date: Published on Aug 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17061
• PDF: https://arxiv.org/pdf/2508.17061
• Github: https://github.com/stefanos50/REGEN
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17061
• PDF: https://arxiv.org/pdf/2508.17061
• Github: https://github.com/stefanos50/REGEN
🔹 Datasets citing this paper:
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❤2
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❤4
🔹 Title: If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition
🔹 Publication Date: Published on Aug 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16838
• PDF: https://arxiv.org/pdf/2508.16838
• Github: https://github.com/dipta007/De-Presuppose
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16838
• PDF: https://arxiv.org/pdf/2508.16838
• Github: https://github.com/dipta007/De-Presuppose
🔹 Datasets citing this paper:
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🔹 Title: MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18264
• PDF: https://arxiv.org/pdf/2508.18264
• Project Page: https://project.ironieser.cc/mmtok
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18264
• PDF: https://arxiv.org/pdf/2508.18264
• Project Page: https://project.ironieser.cc/mmtok
🔹 Datasets citing this paper:
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🔹 Title: Hermes 4 Technical Report
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18255
• PDF: https://arxiv.org/pdf/2508.18255
• Project Page: https://hermes4.nousresearch.com/
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18255
• PDF: https://arxiv.org/pdf/2508.18255
• Project Page: https://hermes4.nousresearch.com/
🔹 Datasets citing this paper:
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❤1
🔹 Title: Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
🔹 Publication Date: Published on Aug 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17326
• PDF: https://arxiv.org/pdf/2508.17326
• Github: https://github.com/tristan-deep/semantic-diffusion-echo-dehazing
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🔹 Publication Date: Published on Aug 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17326
• PDF: https://arxiv.org/pdf/2508.17326
• Github: https://github.com/tristan-deep/semantic-diffusion-echo-dehazing
🔹 Datasets citing this paper:
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🔹 Title: Understanding Tool-Integrated Reasoning
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19201
• PDF: https://arxiv.org/pdf/2508.19201
• Project Page: https://zhongwenxu.notion.site/Understanding-Tool-Integrated-Reasoning-2551c4e140e3805489fadcc802a1ea83
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19201
• PDF: https://arxiv.org/pdf/2508.19201
• Project Page: https://zhongwenxu.notion.site/Understanding-Tool-Integrated-Reasoning-2551c4e140e3805489fadcc802a1ea83
🔹 Datasets citing this paper:
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🔹 Title: Spacer: Towards Engineered Scientific Inspiration
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17661
• PDF: https://arxiv.org/pdf/2508.17661
• Github: https://github.com/asteromorph-corp/spacer
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17661
• PDF: https://arxiv.org/pdf/2508.17661
• Github: https://github.com/asteromorph-corp/spacer
🔹 Datasets citing this paper:
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🔹 Title: VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19247
• PDF: https://arxiv.org/pdf/2508.19247
• Project Page: https://huanngzh.github.io/VoxHammer-Page/
• Github: https://github.com/Nelipot-Lee/VoxHammer/Edit3D-Bench
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/huanngzh/Edit3D-Bench
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🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19247
• PDF: https://arxiv.org/pdf/2508.19247
• Project Page: https://huanngzh.github.io/VoxHammer-Page/
• Github: https://github.com/Nelipot-Lee/VoxHammer/Edit3D-Bench
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/huanngzh/Edit3D-Bench
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❤1
🔹 Title: Unraveling the cognitive patterns of Large Language Models through module communities
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18192
• PDF: https://arxiv.org/pdf/2508.18192
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🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18192
• PDF: https://arxiv.org/pdf/2508.18192
🔹 Datasets citing this paper:
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❤2
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🔹 Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/
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🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/
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❤1
🔹 Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network
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🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network
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