🔹 Title: Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face

🔹 Publication Date: Published on Aug 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06811
• PDF: https://arxiv.org/pdf/2508.06811

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🔹 Title: Matrix-3D: Omnidirectional Explorable 3D World Generation

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08086
• PDF: https://arxiv.org/pdf/2508.08086

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🔹 Title: Test-Time Reinforcement Learning for GUI Grounding via Region Consistency

🔹 Publication Date: Published on Aug 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05615
• PDF: https://arxiv.org/pdf/2508.05615
• Project Page: https://zju-real.github.io/gui-rcpo/
• Github: https://github.com/zju-real/gui-rcpo

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🔹 Title: UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation

🔹 Publication Date: Published on Aug 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05399
• PDF: https://arxiv.org/pdf/2508.05399

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🔹 Title: Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09138
• PDF: https://arxiv.org/pdf/2508.09138
• Project Page: https://aim-uofa.github.io/dLLM-MidTruth/
• Github: https://github.com/aim-uofa/dLLM-MidTruth

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🔹 Title: Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07976v1
• PDF: https://arxiv.org/pdf/2508.07976
• Github: https://github.com/inclusionAI/ASearcher

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🔹 Title: HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08088
• PDF: https://arxiv.org/pdf/2508.08088
• Github: https://github.com/plageon/HierSearch

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/zstanjj/HierSearch-Datasets

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🔹 Title: AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09101
• PDF: https://arxiv.org/pdf/2508.09101

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🔹 Title: WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

🔹 Publication Date: Published on Aug 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.05748
• PDF: https://arxiv.org/pdf/2508.05748
• Github: https://github.com/Alibaba-NLP/WebAgent

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🔹 Title: Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08791
• PDF: https://arxiv.org/pdf/2508.08791
• Github: https://github.com/bytedance/FTRL

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🔹 Title: CharacterShot: Controllable and Consistent 4D Character Animation

🔹 Publication Date: Published on Aug 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07409
• PDF: https://arxiv.org/pdf/2508.07409
• Github: https://github.com/Jeoyal/CharacterShot

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🔹 Title: Cut2Next: Generating Next Shot via In-Context Tuning

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08244
• PDF: https://arxiv.org/pdf/2508.08244
• Project Page: https://vchitect.github.io/Cut2Next-project/

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🔹 Title: Bridging Theory and Practice in Quantum Game Theory: Optimized Implementation of the Battle of the Sexes with Error Mitigation on NISQ Hardware

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09050
• PDF: https://arxiv.org/pdf/2508.09050

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🔹 Title: Train Long, Think Short: Curriculum Learning for Efficient Reasoning

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08940
• PDF: https://arxiv.org/pdf/2508.08940
• Github: https://github.com/hammoudhasan/

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🔹 Title: OpenCUA: Open Foundations for Computer-Use Agents

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09123
• PDF: https://arxiv.org/pdf/2508.09123

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/xlangai/AgentNet

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🔹 Title: Aryabhata: An exam-focused language model for JEE Math

🔹 Publication Date: Published on Aug 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08665
• PDF: https://arxiv.org/pdf/2508.08665

🔹 Datasets citing this paper:
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• https://huggingface.co/spaces/PhysicsWallahAI/Aryabhata-Demo
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🔹 Title: NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations

🔹 Publication Date: Published on Aug 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04195
• PDF: https://arxiv.org/pdf/2508.04195
• Github: https://nvspeech170k.github.io/

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🔹 Title: Adversarial Video Promotion Against Text-to-Video Retrieval

🔹 Publication Date: Published on Aug 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06964
• PDF: https://arxiv.org/pdf/2508.06964
• Github: https://github.com/michaeltian108/ViPro

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🔹 Title: WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion

🔹 Publication Date: Published on Aug 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.06485
• PDF: https://arxiv.org/pdf/2508.06485
• Github: https://github.com/Sofianebouaziz1/WGAST

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🔹 Title: GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay

🔹 Publication Date: Published on Aug 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04676
• PDF: https://arxiv.org/pdf/2508.04676
• Github: https://github.com/Qznan/GeRe

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🔹 Title: Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability

🔹 Publication Date: Published on Aug 6

🔹 Abstract: ISEval framework evaluates large multimodal models' ability to detect flawed inputs, revealing challenges in identifying certain types of errors and modality-specific biases. AI-generated summary 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.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04017

• PDF: https://arxiv.org/pdf/2508.04017

• Github: https://github.com/MLGroupJLU/LMM_ISEval

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