Probing an Embodied LLM: When Higher Observation Fidelity Hurts Problem Solving

arXiv:2605.20072v2 Announce Type: replace Abstract: Large Language Models (LLMs) 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 an embodied LLM agent behaviorally by varying the available information and measuring the resulting changes in behavior. Using the Lockbox, a sequential mechanical puzzle with hidden interdependencies, we evaluate LLMs across RGB, RGB-D, and ground-truth symbolic observations in a phy

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