✨Optimizing Few-Step Generation with Adaptive Matching Distillation

📝 Summary:
Adaptive Matching Distillation AMD improves generative model training by detecting and escaping unstable optimization regions. It uses reward proxies to correct trajectories, boosting sample fidelity and training robustness across generation tasks.

🔹 Publication Date: Published on Feb 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07345
• PDF: https://arxiv.org/pdf/2602.07345

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨BiManiBench: A Hierarchical Benchmark for Evaluating Bimanual Coordination of Multimodal Large Language Models

📝 Summary:
BiManiBench evaluates multimodal large language models on bimanual robotic tasks, revealing limitations in spatial grounding and control despite strong high-level reasoning capabilities. AI-generated ...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08392
• PDF: https://arxiv.org/pdf/2602.08392
• Project Page: https://bimanibench.github.io/
• Github: https://github.com/bimanibench/BiManiBench

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

📝 Summary:
Agent S2 is a new compositional framework for computer use agents. It uses Mixture-of-Grounding and Proactive Hierarchical Planning to achieve state-of-the-art performance across various benchmarks and operating systems, significantly improving automation.

🔹 Publication Date: Published on Apr 1, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.00906
• PDF: https://arxiv.org/pdf/2504.00906
• Project Page: https://www.simular.ai/articles/agent-s2-technical-review
• Github: https://github.com/simular-ai/Agent-S

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#AI #AIagents #Automation #MachineLearning #ComputerScience

✨Reinforced Fast Weights with Next-Sequence Prediction

📝 Summary:
REFINE is an RL framework that improves fast weight models for long-context tasks. It uses next-sequence prediction NSP instead of next-token prediction, enhancing long-range dependency capture. Experiments show it consistently outperforms supervised fine-tuning.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16704
• PDF: https://arxiv.org/pdf/2602.16704
• Github: https://github.com/princetonvisualai/ReFINE/

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#ReinforcementLearning #MachineLearning #DeepLearning #NLP #AI

✨Learning Personalized Agents from Human Feedback

📝 Summary:
PAHF enables AI agents to continually personalize through explicit user memory and dual feedback. It rapidly adapts to changing user preferences by integrating pre-action clarification and post-action updates, significantly reducing personalization error and improving learning speed.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16173
• PDF: https://arxiv.org/pdf/2602.16173
• Project Page: https://personalized-ai.github.io/
• Github: https://github.com/facebookresearch/PAHF

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#AI #Personalization #HumanAIInteraction #MachineLearning #AIAgents

✨OPBench: A Graph Benchmark to Combat the Opioid Crisis

📝 Summary:
OPBench is the first comprehensive graph benchmark to systematically evaluate graph learning methods for the opioid crisis. It includes five diverse datasets across three domains and a unified framework, providing insights for future research.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14602
• PDF: https://arxiv.org/pdf/2602.14602
• Github: https://github.com/Tianyi-Billy-Ma/OPBench

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#OpioidCrisis #GraphLearning #DataScience #MachineLearning #PublicHealth

✨Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5

📝 Summary:
This report assesses frontier AI risks, updating granular scenarios for cyber offense, manipulation, deception, uncontrolled AI R&D, and self-replication. It also proposes robust mitigation strategies for secure deployment of advanced AI systems.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14457
• PDF: https://arxiv.org/pdf/2602.14457
• Project Page: https://ai45lab.github.io/safeworkf1-page/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning

📝 Summary:
A trainable sparse attention method called SpargeAttention2 is proposed that achieves high sparsity in diffusion models while maintaining generation quality through hybrid masking rules and distillati...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13515
• PDF: https://arxiv.org/pdf/2602.13515

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

📝 Summary:
GUI-Owl-1.5 is a multi-platform GUI agent model with varying sizes that achieves superior performance across GUI automation, grounding, tool-calling, and memory tasks through innovations in data pipel...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16855
• PDF: https://arxiv.org/pdf/2602.16855
• Project Page: https://github.com/X-PLUG/MobileAgent/tree/main/Mobile-Agent-v3.5
• Github: https://github.com/X-PLUG/MobileAgent

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers

📝 Summary:
Dynamic tokenization improves diffusion transformer efficiency by adjusting patch sizes based on content complexity and denoising timestep, achieving significant speedup without quality loss. AI-gener...

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16968
• PDF: https://arxiv.org/pdf/2602.16968

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#AI #DataScience #MachineLearning #HuggingFace #Research