2024/04/08 by Shen Gao, Hao Li, Gao, Shen +11 · 1 citation
Computer Science · Psychology · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Multiagent Systems (cs.MA) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2404.05569
openalex publication_date 2024/04/08 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance the capability of the agents. We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance. In this paper, we propose Reusable Experience Accumulation with 360^∘ Assessment (360^∘REA), a hierarchical multi-agent framework inspired by corporate organizational practices. The framework employs a novel 360^∘ performance assessment method for multi-perspective performance evaluation with fine-grained assessment. To enhance the capability of agents in addressing complex tasks, we introduce dual-level experience pool for agents to accumulate experience through fine-grained assessment. Extensive experiments on complex task datasets demonstrate the effectiveness of 360^∘REA.