Researchers investigated the performance of embodied LLMs in a physical robotic setup with varying levels of observation fidelity. They found that LLMs performed best under raw RGB input and worst under perfect ground-truth observations. This suggests that measured performance may not reflect robust problem-solving abilities, but rather the interaction between perceptual errors and reasoning failures.