360°REA: Towards A Reusable Experience Accumulation with 360°
  Assessment for Multi-Agent System
Shen Gao Hao Li Zhengliang Shi Chengrui Huang Quan Tu Zhiliang Tian Minlie Huang Shuo Shang
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{\deg} Assessment (360{\deg}REA), a hierarchical multi-agent framework
inspired by corporate organizational practices. The framework employs a novel
360{\deg} 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{\deg}REA.