Author

Alan L. Yuille

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Jul 2026

The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory

Enter--Propagation--Recovery (E-P-R), a trajectory-level framework that asks where memory first changes an action, whether that change carries forward, and whether the agent can recover after leaving a correct path is proposed, to diagnose how models consume retrieved memory across a multi-step action trajectory.

Yi-Xiong Chen, Xin-Yi Bai, Alan L. Yuille · arXiv.org · 0 citations

#robotics Jul 2026

Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.

Guan-Xiong Chen, Qian-Jun Xia, Jia-Wei Peng et al. · arXiv.org · 0 citations