✨Chain of Mindset: Reasoning with Adaptive Cognitive Modes

📝 Summary:
A novel training-free framework called Chain of Mindset enables step-level adaptive mindset orchestration for large language models by integrating spatial, convergent, divergent, and algorithmic reaso...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10063
• PDF: https://arxiv.org/pdf/2602.10063
• Github: https://github.com/QuantaAlpha/chain-of-mindset

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✨VideoWorld 2: Learning Transferable Knowledge from Real-world Videos

📝 Summary:
VideoWorld 2 enables transferable knowledge learning from raw videos through a dynamic-enhanced Latent Dynamics Model that decouples action dynamics from visual appearance, achieving improved task per...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10102
• PDF: https://arxiv.org/pdf/2602.10102
• Project Page: https://maverickren.github.io/VideoWorld2.github.io/
• Github: https://github.com/ByteDance-Seed/VideoWorld/tree/main/VideoWorld2

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✨BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generation

📝 Summary:
BagelVLA is a unified Vision-Language-Action model that integrates linguistic planning, visual forecasting, and action generation through residual flow guidance for improved manipulation tasks. AI-gen...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09849
• PDF: https://arxiv.org/pdf/2602.09849
• Project Page: https://cladernyjorn.github.io/BagelVLA.github.io/

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✨DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents

📝 Summary:
Diffusion Large Language Models are optimized for search agents through enhanced reasoning capabilities and reduced latency via a parallel reasoning paradigm. AI-generated summary Recently, Diffusion ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07035
• PDF: https://arxiv.org/pdf/2602.07035
• Project Page: https://bubble65.github.io/dllm-searcher-pub/
• Github: https://github.com/bubble65/DLLM-Searcher

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✨Covo-Audio Technical Report

📝 Summary:
Covo-Audio is a 7B-parameter end-to-end large audio language model that processes continuous audio inputs and generates audio outputs, achieving state-of-the-art performance across speech-text modelin...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09823
• PDF: https://arxiv.org/pdf/2602.09823

==================================

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✨OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration

📝 Summary:
Reinforcement learning with verifiable rewards is used to enhance parallel thinking in large reasoning models through outline-guided path exploration that reduces information redundancy and improves s...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08344
• PDF: https://arxiv.org/pdf/2602.08344

==================================

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✨SAGE: Scalable Agentic 3D Scene Generation for Embodied AI

📝 Summary:
SAGE is an agentic framework that automatically generates simulation-ready 3D environments for embodied AI by combining layout and object composition generators with evaluative critics for semantic pl...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10116
• PDF: https://arxiv.org/pdf/2602.10116
• Project Page: https://nvlabs.github.io/sage

✨ Datasets citing this paper:
• https://huggingface.co/datasets/nvidia/SAGE-10k

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✨Autoregressive Image Generation with Masked Bit Modeling

📝 Summary:
Discrete tokenizers can match or exceed continuous methods when properly scaled, and a new masked Bit AutoRegressive modeling approach achieves state-of-the-art results with reduced computational cost...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09024
• PDF: https://arxiv.org/pdf/2602.09024
• Project Page: https://bar-gen.github.io/
• Github: https://bar-gen.github.io/

==================================

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✨SHARP: Social Harm Analysis via Risk Profiles for Measuring Inequities in Large Language Models

📝 Summary:
Large language models exhibit varying levels of social risk across multiple dimensions, with significant differences in worst-case behavior that cannot be captured by traditional scalar evaluation met...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21235
• PDF: https://arxiv.org/pdf/2601.21235

==================================

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✨VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model

📝 Summary:
VLA-JEPA is a JEPA-style pretraining framework that improves vision-language-action policy learning by using leakage-free state prediction in latent space, enhancing generalization and robustness in m...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10098
• PDF: https://arxiv.org/pdf/2602.10098
• Project Page: https://ginwind.github.io/VLA-JEPA/
• Github: https://github.com/ginwind/VLA-JEPA

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✨OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration

📝 Summary:
OPUS is a dynamic data selection framework that improves pre-training efficiency by scoring data candidates based on optimizer-induced update projections in a stable proxy-derived target space, achiev...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05400
• PDF: https://arxiv.org/pdf/2602.05400

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✨Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

📝 Summary:
Large language model agents trained in synthetic environments with code-driven simulations and database-backed state transitions demonstrate superior out-of-distribution generalization compared to tra...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10090
• PDF: https://arxiv.org/pdf/2602.10090
• Project Page: https://github.com/Snowflake-Labs/agent-world-model
• Github: https://github.com/Snowflake-Labs/agent-world-model

🔹 Models citing this paper:
• https://huggingface.co/Snowflake/Arctic-AWM-4B
• https://huggingface.co/Snowflake/Arctic-AWM-8B
• https://huggingface.co/Snowflake/Arctic-AWM-14B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K

==================================

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✨Olaf-World: Orienting Latent Actions for Video World Modeling

📝 Summary:
Sequence-level control-effect alignment enables structured latent action space learning for zero-shot action transfer in video world models. AI-generated summary Scaling action-controllable world mode...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10104
• PDF: https://arxiv.org/pdf/2602.10104

==================================

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✨Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems

📝 Summary:
Dr. MAS solves reinforcement learning instability in multi-agent LLM systems. It normalizes advantages per agent using individual reward statistics, calibrating gradients. This stabilizes training, eliminates spikes, and significantly boosts performance.

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08847
• PDF: https://arxiv.org/pdf/2602.08847
• Github: https://github.com/langfengQ/DrMAS

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✨Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning

📝 Summary:
A large-scale, high-quality, and fully open dataset for text-to-image fine-tuning is presented, featuring over 6 million text-image pairs with rigorous filtering for alignment and quality across multi...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09439
• PDF: https://arxiv.org/pdf/2602.09439
• Project Page: https://huggingface.co/spaces/ma-xu/fine-t2i-explore

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ma-xu/fine-t2i

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ma-xu/fine-t2i-explore

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✨P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads

📝 Summary:
Physics-oriented vision-language models leverage curriculum reinforcement learning and agentic augmentation to achieve state-of-the-art scientific reasoning performance while maintaining physical cons...

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09443
• PDF: https://arxiv.org/pdf/2602.09443
• Project Page: https://prime-rl.github.io/P1-VL
• Github: https://github.com/PRIME-RL/P1-VL

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✨ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training

📝 Summary:
ScaleEnv framework generates interactive environments from scratch to improve agent generalization through diverse domain scaling and verified task completion. AI-generated summary Training generalist...

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06820
• PDF: https://arxiv.org/pdf/2602.06820

==================================

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✨Learning Self-Correction in Vision-Language Models via Rollout Augmentation

📝 Summary:
Octopus, an RL rollout augmentation framework, enables efficient self-correction learning in vision-language models through synthetic example generation and response masking strategies. AI-generated s...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08503
• PDF: https://arxiv.org/pdf/2602.08503
• Project Page: https://dripnowhy.github.io/Octopus/

🔹 Models citing this paper:
• https://huggingface.co/Tuwhy/Octopus-8B

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✨SafePred: A Predictive Guardrail for Computer-Using Agents via World Models

📝 Summary:
SafePred is a predictive guardrail framework for computer-using agents that uses risk prediction and decision optimization to prevent both immediate and delayed high-risk consequences in complex envir...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01725
• PDF: https://arxiv.org/pdf/2602.01725
• Github: https://github.com/YurunChen/SafePred

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✨Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss

📝 Summary:
This study refines autoregressive image generation with diffusion loss, showing patch denoising effectively mitigates condition errors. A novel Optimal Transport based condition refinement method is introduced to ensure convergence to an ideal condition distribution, outperforming prior methods.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07022
• PDF: https://arxiv.org/pdf/2602.07022

==================================

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