✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#ImageGeneration #DiffusionModels #AutoregressiveModels #OptimalTransport #MachineLearning

✨Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning

📝 Summary:
This paper introduces a cognitive-inspired framework for long-context LLM reasoning. It uses chunk-wise memory compression and selective recall, optimized via end-to-end reinforcement learning to improve accuracy and efficiency for contexts up to 1.75M tokens.

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08382
• PDF: https://arxiv.org/pdf/2602.08382

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

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#LLM #ReinforcementLearning #LongContext #MemoryCompression #AIResearch

✨Stable Velocity: A Variance Perspective on Flow Matching

📝 Summary:
Stable Velocity tackles high-variance training in flow matching by identifying low-variance regimes. It introduces StableVM and VA-REPA for more efficient training, and StableVS for over 2x faster sampling. This improves both training and inference without compromising quality.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05435
• PDF: https://arxiv.org/pdf/2602.05435
• Project Page: https://linydthu.github.io/StableVelocity/
• Github: https://github.com/linYDTHU/StableVelocity

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

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#FlowMatching #GenerativeAI #MachineLearning #DeepLearning #VarianceReduction

✨TodoEvolve: Learning to Architect Agent Planning Systems

📝 Summary:
TodoEvolve enables autonomous synthesis and revision of task-specific planning architectures through a modular design space and multi-objective reinforcement learning optimization. AI-generated summar...

🔹 Publication Date: Published on Feb 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07839
• PDF: https://arxiv.org/pdf/2602.07839
• Github: https://github.com/EcthelionLiu/TodoEvolve

🔹 Models citing this paper:
• https://huggingface.co/EcthelionLiu/Todo-14B

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✨Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs

📝 Summary:
STEER2ADAPT adapts LLMs by composing steering vectors from reusable semantic prior subspaces. This lightweight framework dynamically combines basis vectors, offering efficient and flexible adaptation for complex tasks without learning new vectors. It achieves an average performance improvement of...

🔹 Publication Date: Published on Feb 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07276
• PDF: https://arxiv.org/pdf/2602.07276

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

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#LLM #AI #MachineLearning #ModelAdaptation #SteeringVectors

✨From Directions to Regions: Decomposing Activations in Language Models via Local Geometry

📝 Summary:
Mixture of Factor Analyzers MFA models language model activations via local Gaussian regions, capturing complex nonlinear structures. MFA outperforms baselines, improving localization and steering, positioning local geometry as a promising unit for concept discovery and control.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02464
• PDF: https://arxiv.org/pdf/2602.02464
• Github: https://github.com/ordavid-s/decomposing-activations-local-geometry

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

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#LLM #AIResearch #Interpretability #NeuralNetworks #MachineLearning

✨TreeCUA: Efficiently Scaling GUI Automation with Tree-Structured Verifiable Evolution

📝 Summary:
TreeCUA scales GUI automation by organizing CUA exploration trajectories into tree structures. It uses multi-agent collaboration, adaptive exploration, and verification to improve GUI planning. This approach achieves better efficiency, generalization, and enhances planning capabilities.

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09662
• PDF: https://arxiv.org/pdf/2602.09662
• Github: https://github.com/UITron-hub/TreeCUA

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

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#GUIAutomation #AI #SoftwareAutomation #RPA #Planning

✨Rethinking Global Text Conditioning in Diffusion Transformers

📝 Summary:
Conventional text conditioning pooled embedding in diffusion transformers offers little benefit alone. But, when used as training-free guidance for controllable generation, it significantly improves performance across text-to-image, video, and image editing tasks.

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09268
• PDF: https://arxiv.org/pdf/2602.09268
• Github: https://github.com/quickjkee/modulation-guidance

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

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#DiffusionModels #GenerativeAI #AIResearch #ComputerVision #MachineLearning

✨Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

📝 Summary:
COVER stops flip-flop oscillations in parallel diffusion decoding with cache override verification. It performs leave-one-out verification and stable drafting in one pass, preserving context via KV cache override. This greatly reduces revisions for faster, quality-preserving decoding.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06161
• PDF: https://arxiv.org/pdf/2602.06161

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

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#DiffusionModels #GenerativeAI #DeepLearning #Decoding #ContextPreservation