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HCPG-Flow boosts robot manipulation success rates by over 9% by intelligently guiding action selection based on task progress rather than just end-effector positions.
Natural paper revisions can be harnessed to train AI agents for precise and context-aware editing of complex scientific diagrams.
Open-AoE transforms egocentric video capture into a powerful resource for embodied intelligence, making it easier than ever to train robots with human-like manipulation skills.
Current LLMs only achieve 27.3% accuracy in reasoning about scientific lineage, revealing a critical gap in their compositional capabilities.
Proactive VideoLLMs can finally be both accurate AND efficient thanks to a novel propose-match framework that decouples semantic understanding from streaming perception.
