Reflex First, Reflect Later: Latency-Aware Embodied LLM Agents for Dynamic Response 文章

arXiv:2506.07223v2 Announce Type: replace Abstract: Large language models (LLMs) have substantially improved the planning capabilities of embodied agents, enabling their deployment in dynamic and safety-critical environments. However, these settings expose a critical limitation: inference latency. Delayed LLM responses can weaken real-time responsiveness and misalign agent reasoning with rapidly changing environmental states. This paper systematically studies the impact of inference latency on LLM-based embodied agents in dynamic environments. We introduce an FPS-based Time Conversion Mechanism (TCM) that maps inference time to elapsed simulation time, allowing computational delays to directly affect environmental evolution and agent outcomes. We instantiate this protocol in HAZARD and introduce Response Latency (RL) and Latency-to-Action Ratio (LAR) to evaluate agent responsiveness.