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.

大型语言模型代理在各种复杂任务中展示出了显著的进展。最近的研究集中在优化代理团队或使用自我反思来迭代解决复杂任务。本文提出了一种灵感来自公司组织实践的层次化多代理框架，360° 可重复利用经验积累评估（360°REA）。该框架使用一种新颖的 360 度绩效评估方法，进行多角度的绩效评估和细粒度的评估。为了增强代理在处理复杂任务时的能力，我们引入了双层经验池，使代理可以通过细粒度评估来积累经验。对复杂任务数据集的大量实验证明了 360°REA 的有效性。