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It is argued that harnessing physical-digital AI agents, through persistent coupling of digital intelligence with physical sensing, dynamics, action, and feedback, provides a system-level pathway toward adaptive and continuously evolving UAV autonomy.
MLLMs struggle to juggle proactive tasks and reactive queries in dynamic video streams, but a simple agentic framework can significantly improve their coordination without any training.
Forget static datasets – RL-based co-training unlocks +20% real-world VLA performance by interactively leveraging simulation while preserving real-world capabilities.
