360$^\circ$REA: Towards A Reusable Experience Accumulation with 360\deg Assessment for Multi-Agent System
<|reference_start|>360$^\circ$REA: Towards A Reusable Experience Accumulation with 360\deg Assessment for Multi-Agent System: 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.<|reference_end|>
@article{gao2024360$^\circ$rea:,
    title={360$^\circ$REA: Towards A Reusable Experience Accumulation with
  360{\deg} Assessment for Multi-Agent System},
    author={Shen Gao, Hao Li, Chengrui Huang, Quan Tu, Zhiliang Tian, Minlie
  Huang, Shuo Shang},
    journal={arXiv preprint arXiv:2404.05569},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05569},
    primaryClass={cs.AI cs.CL cs.MA}
}