AI RESEARCH
Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving
arXiv CS.AI
•
ArXi:2605.20072v1 Announce Type: new Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain success or failure in closed-loop embodied tasks. Following an empirical AI methodology, we study embodied LLM agents behaviorally by varying the information available to the agent and measuring the resulting changes in behavior.