360$^\circ$REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System

• URL: http://arxiv.org/abs/2404.05569v2
• Date: Wed, 26 Jun 2024 11:42:10 GMT
• Title: 360$^\circ$REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
• Authors: Shen Gao, Hao Li, Chengrui Huang, Quan Tu, Zhiliang Tian, Minlie Huang, Shuo Shang,
• Abstract summary: We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance. We propose Reusable Experience Accumulation with 360$circ$ Assessment (360$circ$REA), a hierarchical multi-agent framework inspired by corporate organizational practices.
• Score: 71.96888731208838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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$^\circ$ Assessment (360$^\circ$REA), a hierarchical multi-agent framework inspired by corporate organizational practices. The framework employs a novel 360$^\circ$ 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$^\circ$REA.

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