让智能体自主进化角色，实现去中心化协作。
MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration
- 智能体通过自我优化的个人档案动态调整能力。
- 在复杂任务中表现优于现有框架，适应变化更强。
- 适合需要自适应协同的智能系统研发者。
基于大语言模型的多智能体系统在处理复杂任务方面展现出潜力，但通常依赖预设角色和集中式协调，限制了其对动态挑战的适应能力。本文提出MorphAgent，一种自主、自组织、自适应的去中心化多智能体协作系统，使智能体能够动态演化自身角色与能力。该方法通过三个关键指标优化智能体的自我演化档案，指导个体持续精进专长，同时保持团队互补性。MorphAgent采用两阶段流程：第一阶段为档案更新，优化智能体配置；第二阶段为任务执行，智能体根据任务反馈实时调整角色。实验结果表明，MorphAgent在任务性能和应对需求变化方面均优于现有框架，为更鲁棒、更灵活的多智能体协作系统提供了新路径。
原文摘要 · Abstract (English)
Large Language Model (LLM) based multi-agent systems (MAS) have shown promise in tackling complex tasks, but often rely on predefined roles and centralized coordination, limiting their adaptability to evolving challenges. This paper introduces MorphAgent, a novel Autonomous, Self-Organizing, and Self-Adaptive Multi-Agent System for decentralized agent collaboration that enables agents to dynamically evolve their roles and capabilities. Our approach employs self-evolving agent profiles, optimized through three key metrics, guiding agents in refining their individual expertise while maintaining complementary team dynamics. MorphAgent implements a two-phase process: a Profile Update phase for profile optimization, followed by a Task Execution phase where agents continuously adapt their roles based on task feedback. Our experimental results show that MorphAgent outperforms existing frameworks in terms of task performance and adaptability to changing requirements, paving the way for more robust and versatile multi-agent collaborative systems.
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