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cs.CV2026
Proactive Physical AI through Continuous Observation and Timely Intervention
Zhenhua Yang, Jiafeng Liang, Xingyu Zhou +26
Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real-world settings, however, im…
cs.AI2026
AutoTool: Automatic Scaling of Tool-Use Capabilities in RL via Decoupled Entropy Constraints
Yirong Zeng, Xiao Ding, Yufei Liu +9
Tool use represents a critical capability for AI agents, with recent advances focusing on leveraging reinforcement learning (RL) to scale up the explicit reasoning process to achie…