New LLM agent framework tackles inference latency for dynamic environments

Researchers have developed a new framework to address the challenge of inference latency in large language models (LLMs) used by embodied agents. The proposed system, called the Rapid-Reflex Async-Reflect Agent (RRARA), integrates quick reflexive actions with delayed LLM reflections to improve responsiveness in dynamic environments. This approach accounts for computational delays by mapping inference time to elapsed simulation time, thereby mitigating errors caused by misaligned reasoning with rapidly changing states. Experiments demonstrate that RRARA achieves a better balance between decision quality and responsiveness compared to traditional methods. AI

IMPACT This research could lead to more responsive and reliable AI agents in real-world, dynamic environments.

RANK_REASON The cluster contains a research paper detailing a novel framework for LLM-based embodied agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• FPS-based Time Conversion Mechanism (TCM)
• Latency-to-Action Ratio (LAR)
• LLM-based PrePlanner
• Rapid-Reflex Async-Reflect Agent (RRARA)
• Reflex First, Reflect Later: Latency-Aware Embodied LLM Agents for Dynamic Response
• Response Latency (RL)
• Yangqing Zheng

• paper
• model release

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