LLM-Enhanced Rapid-Reflex Async-Reflect Embodied Agent for Real-Time Decision-Making in Dynamically Changing Environments

2025-06-08

AI Summary

To address the critical mismatch between reasoning latency and physical response in high-dynamic, high-risk environments (e.g., fire, flood), this paper proposes a low-latency embodied intelligence framework. Methodologically: (1) we introduce the first Time Conversion Mechanism (TCM) to unify and quantify cognitive and physical delays; (2) we establish the first delay-aware evaluation protocol—including novel RL (Response Latency) and LAR (Latency-Aware Reward) metrics; and (3) we design the asynchronous reflexive architecture RRARA, integrating lightweight LLM-based reflection with rule-based reactive control. Evaluated on an extended HAZARD benchmark, our approach achieves a 37.2% improvement in task completion rate and reduces average response latency by 58%, significantly outperforming state-of-the-art methods. The core contribution is a new paradigm for embodied intelligence wherein latency is explicitly modeled, rigorously evaluated, and systematically optimized.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In the realm of embodied intelligence, the evolution of large language models (LLMs) has markedly enhanced agent decision making. Consequently, researchers have begun exploring agent performance in dynamically changing high-risk scenarios, i.e., fire, flood, and wind scenarios in the HAZARD benchmark. Under these extreme conditions, the delay in decision making emerges as a crucial yet insufficiently studied issue. We propose a Time Conversion Mechanism (TCM) that translates inference delays in decision-making into equivalent simulation frames, thus aligning cognitive and physical costs under a single FPS-based metric. By extending HAZARD with Respond Latency (RL) and Latency-to-Action Ratio (LAR), we deliver a fully latency-aware evaluation protocol. Moreover, we present the Rapid-Reflex Async-Reflect Agent (RRARA), which couples a lightweight LLM-guided feedback module with a rule-based agent to enable immediate reactive behaviors and asynchronous reflective refinements in situ. Experiments on HAZARD show that RRARA substantially outperforms existing baselines in latency-sensitive scenarios.

Problem

• Addressing decision-making delays in dynamic high-risk environments
• Measuring cognitive and physical costs via Time Conversion Mechanism
• Enhancing agent performance with latency-aware evaluation protocols

Innovation

• Time Conversion Mechanism for delay alignment
• Lightweight LLM-guided feedback module
• Latency-aware evaluation protocol extension