🔹 Title: Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08896
• PDF: https://arxiv.org/pdf/2508.08896
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08896
• PDF: https://arxiv.org/pdf/2508.08896
🔹 Datasets citing this paper:
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❤2
🔹 Title: BiasGym: Fantastic Biases and How to Find (and Remove) Them
🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08855
• PDF: https://arxiv.org/pdf/2508.08855
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🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08855
• PDF: https://arxiv.org/pdf/2508.08855
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❤2
🔹 Title: Multi-human Interactive Talking Dataset
🔹 Publication Date: Published on Aug 5
🔹 Abstract: MIT, a large-scale dataset for multi-human talking video generation, includes fine-grained annotations and is used to demonstrate CovOG, a baseline model integrating a Multi-Human Pose Encoder and an Interactive Audio Driver. AI-generated summary Existing studies on talking video generation have predominantly focused on single-person monologues or isolated facial animations, limiting their applicability to realistic multi-human interactions. To bridge this gap, we introduce MIT, a large-scale dataset specifically designed for multi-human talking video generation. To this end, we develop an automatic pipeline that collects and annotates multi-person conversational videos. The resulting dataset comprises 12 hours of high-resolution footage, each featuring two to four speakers, with fine-grained annotations of body poses and speech interactions. It captures natural conversational dynamics in multi-speaker scenario, offering a rich resource for studying interactive visual behaviors. To demonstrate the potential of MIT, we furthur propose CovOG, a baseline model for this novel task. It integrates a Multi-Human Pose Encoder (MPE) to handle varying numbers of speakers by aggregating individual pose embeddings, and an Interactive Audio Driver (IAD) to modulate head dynamics based on speaker-specific audio features. Together, these components showcase the feasibility and challenges of generating realistic multi-human talking videos, establishing MIT as a valuable benchmark for future research. The code is avalibale at: https://github.com/showlab/Multi-human-Talking-Video-Dataset.
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03050
• PDF: https://arxiv.org/pdf/2508.03050
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 5
🔹 Abstract: MIT, a large-scale dataset for multi-human talking video generation, includes fine-grained annotations and is used to demonstrate CovOG, a baseline model integrating a Multi-Human Pose Encoder and an Interactive Audio Driver. AI-generated summary Existing studies on talking video generation have predominantly focused on single-person monologues or isolated facial animations, limiting their applicability to realistic multi-human interactions. To bridge this gap, we introduce MIT, a large-scale dataset specifically designed for multi-human talking video generation. To this end, we develop an automatic pipeline that collects and annotates multi-person conversational videos. The resulting dataset comprises 12 hours of high-resolution footage, each featuring two to four speakers, with fine-grained annotations of body poses and speech interactions. It captures natural conversational dynamics in multi-speaker scenario, offering a rich resource for studying interactive visual behaviors. To demonstrate the potential of MIT, we furthur propose CovOG, a baseline model for this novel task. It integrates a Multi-Human Pose Encoder (MPE) to handle varying numbers of speakers by aggregating individual pose embeddings, and an Interactive Audio Driver (IAD) to modulate head dynamics based on speaker-specific audio features. Together, these components showcase the feasibility and challenges of generating realistic multi-human talking videos, establishing MIT as a valuable benchmark for future research. The code is avalibale at: https://github.com/showlab/Multi-human-Talking-Video-Dataset.
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03050
• PDF: https://arxiv.org/pdf/2508.03050
🔹 Datasets citing this paper:
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❤3
🔹 Title: Democratizing Diplomacy: A Harness for Evaluating Any Large Language Model on Full-Press Diplomacy
🔹 Publication Date: Published on Aug 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07485
• PDF: https://arxiv.org/pdf/2508.07485
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07485
• PDF: https://arxiv.org/pdf/2508.07485
🔹 Datasets citing this paper:
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❤3
🔹 Title: TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08680
• PDF: https://arxiv.org/pdf/2508.08680
• Github: https://github.com/ArmelRandy/topxgen
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/almanach/topxgen-gemma-3-27b-and-nllb-3.3b
🔹 Spaces citing this paper:
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🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08680
• PDF: https://arxiv.org/pdf/2508.08680
• Github: https://github.com/ArmelRandy/topxgen
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/almanach/topxgen-gemma-3-27b-and-nllb-3.3b
🔹 Spaces citing this paper:
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❤4
🔹 Title: Optimization-Free Style Transfer for 3D Gaussian Splats
🔹 Publication Date: Published on Aug 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05813
• PDF: https://arxiv.org/pdf/2508.05813
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05813
• PDF: https://arxiv.org/pdf/2508.05813
🔹 Datasets citing this paper:
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❤2
🔹 Title: Improving Masked Style Transfer using Blended Partial Convolution
🔹 Publication Date: Published on Aug 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05769
• PDF: https://arxiv.org/pdf/2508.05769
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05769
• PDF: https://arxiv.org/pdf/2508.05769
🔹 Datasets citing this paper:
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❤3
🔹 Title: Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
🔹 Publication Date: Published on Aug 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06813
• PDF: https://arxiv.org/pdf/2508.06813
🔹 Datasets citing this paper:
No datasets found
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/morganstanley/qqWEN-overview
==================================
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🔹 Publication Date: Published on Aug 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06813
• PDF: https://arxiv.org/pdf/2508.06813
🔹 Datasets citing this paper:
No datasets found
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/morganstanley/qqWEN-overview
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🔹 Title: Complex Logical Instruction Generation
🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09125
• PDF: https://arxiv.org/pdf/2508.09125
• Github: https://github.com/mianzhang/LogicIF
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09125
• PDF: https://arxiv.org/pdf/2508.09125
• Github: https://github.com/mianzhang/LogicIF
🔹 Datasets citing this paper:
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❤1
🔹 Title: RedDino: A foundation model for red blood cell analysis
🔹 Publication Date: Published on Aug 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08180
• PDF: https://arxiv.org/pdf/2508.08180
• Github: https://github.com/Snarci/RedDino
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08180
• PDF: https://arxiv.org/pdf/2508.08180
• Github: https://github.com/Snarci/RedDino
🔹 Datasets citing this paper:
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❤4
🔹 Title: Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answering
🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08974
• PDF: https://arxiv.org/pdf/2508.08974
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08974
• PDF: https://arxiv.org/pdf/2508.08974
🔹 Datasets citing this paper:
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🔹 Title: ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants
🔹 Publication Date: Published on Aug 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03936
• PDF: https://arxiv.org/pdf/2508.03936
• Project Page: https://purcl.github.io/astra-web/
• Github: https://purcl.github.io/astra-web/
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03936
• PDF: https://arxiv.org/pdf/2508.03936
• Project Page: https://purcl.github.io/astra-web/
• Github: https://purcl.github.io/astra-web/
🔹 Datasets citing this paper:
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🔹 Title: Putnam-AXIOM: A Functional and Static Benchmark
🔹 Publication Date: Published on Aug 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08292
• PDF: https://arxiv.org/pdf/2508.08292
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08292
• PDF: https://arxiv.org/pdf/2508.08292
🔹 Datasets citing this paper:
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❤1
🔹 Title: StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation
🔹 Publication Date: Published on Aug 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08248
• PDF: https://arxiv.org/pdf/2508.08248
• Project Page: https://francis-rings.github.io/StableAvatar/
• Github: https://github.com/Francis-Rings/StableAvatar
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08248
• PDF: https://arxiv.org/pdf/2508.08248
• Project Page: https://francis-rings.github.io/StableAvatar/
• Github: https://github.com/Francis-Rings/StableAvatar
🔹 Datasets citing this paper:
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🔹 Title: AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust GAIA Problem Solving
🔹 Publication Date: Published on Aug 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09889
• PDF: https://arxiv.org/pdf/2508.09889
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09889
• PDF: https://arxiv.org/pdf/2508.09889
🔹 Datasets citing this paper:
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🔹 Title: Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery
🔹 Publication Date: Published on Aug 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08401
• PDF: https://arxiv.org/pdf/2508.08401
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08401
• PDF: https://arxiv.org/pdf/2508.08401
🔹 Datasets citing this paper:
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🔹 Title: MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models
🔹 Publication Date: Published on Aug 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06009
• PDF: https://arxiv.org/pdf/2508.06009
• Github: https://github.com/junfeng0288/MathReal
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/junfeng0288/MathReal
🔹 Spaces citing this paper:
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🔹 Publication Date: Published on Aug 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06009
• PDF: https://arxiv.org/pdf/2508.06009
• Github: https://github.com/junfeng0288/MathReal
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/junfeng0288/MathReal
🔹 Spaces citing this paper:
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🔹 Title: IAG: Input-aware Backdoor Attack on VLMs for Visual Grounding
🔹 Publication Date: Published on Aug 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09456
• PDF: https://arxiv.org/pdf/2508.09456
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09456
• PDF: https://arxiv.org/pdf/2508.09456
🔹 Datasets citing this paper:
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🔹 Title: Cooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning for Large Language Models
🔹 Publication Date: Published on Aug 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05613
• PDF: https://arxiv.org/pdf/2508.05613
• Project Page: https://zju-real.github.io/cooper/
• Github: https://github.com/ZJU-REAL/cooper
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05613
• PDF: https://arxiv.org/pdf/2508.05613
• Project Page: https://zju-real.github.io/cooper/
• Github: https://github.com/ZJU-REAL/cooper
🔹 Datasets citing this paper:
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❤1
🔹 Title: Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
🔹 Publication Date: Published on Aug 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09987
• PDF: https://arxiv.org/pdf/2508.09987
• Project Page: https://yejy53.github.io/Echo-4o/
• Github: https://yejy53.github.io/Echo-4o
🔹 Datasets citing this paper:
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🔹 Publication Date: Published on Aug 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09987
• PDF: https://arxiv.org/pdf/2508.09987
• Project Page: https://yejy53.github.io/Echo-4o/
• Github: https://yejy53.github.io/Echo-4o
🔹 Datasets citing this paper:
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