✨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

✨Computer-Using World Model

📝 Summary:
A world model for desktop software that predicts UI state changes through textual description followed by visual synthesis, improving decision quality and execution robustness in computer-using tasks....

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17365
• PDF: https://arxiv.org/pdf/2602.17365

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#AI #DataScience #MachineLearning #HuggingFace #Research