360{\deg}REA: Towards A Reusable Experience Accumulation with 360{\deg} Assessment for Multi-Agent System

arXiv:2404.05569v1 Announce Type: cross
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 …

abstract agent agents arxiv assessment cs.ai cs.cl cs.ma evaluation experience focus language language model large language large language model llm multi-agent solve tasks team type