LLM-based multi-agent systems (MAS) have emerged as an effective paradigm for complex and long-horizon tasks. However, in real-world tasks, MAS often exhibit various failures during execution and such failures are difficult to eliminate during design. This motivates experience-driven MAS evolution, where a system improves based on its own execution experience. Yet such evolution is challenging because MAS experience is prolonged and intricate, interleaving multiple agents' execution chains and communication messages, which makes it difficult to identify what should be improved. To address this challenge, we propose Meta-Team, an experience-driven MAS evolution framework based on collaborative self-evolution. Meta-Team preserves the execution context of each agent and coordinates post-task communication, enabling agents to exchange distributed evidence for evolution. Building on this design, Meta-Team conducts multi-scale self-evolution, transforming execution experience into reusable improvements to agent behaviors, inter-agent coordination, and team-level organization. Across six long-horizon agent benchmarks, Meta-Team consistently outperforms single-agent systems, hand-crafted MAS, and prior MAS evolution methods; further analyses demonstrate that Meta-Team enables more reliable and scalable MAS self-evolution.

翻译：基于大语言模型的多智能体系统已成为处理复杂长周期任务的有效范式。然而在实际应用中，多智能体系统在执行过程中常出现各类故障，且这些缺陷难以在设计阶段消除。这催生了经验驱动的多智能体系统进化范式——系统基于自身执行经验进行改进。但此类进化面临显著挑战：多智能体系统的执行经验具有延展性与复杂性，交织着多个智能体的执行链条与通信消息，导致难以精准定位待改进环节。针对这一难题，我们提出Meta-Team——基于协作式自我进化的经验驱动式多智能体系统进化框架。Meta-Team通过保留各智能体的执行上下文并协调任务后通信，使智能体能够交换分布式进化证据。基于该设计，Meta-Team实现多尺度自我进化：将执行经验转化为智能体行为优化、智能体间协同改进及团队级组织重构等可复用改进。在六个长周期智能体基准测试中，Meta-Team持续优于单智能体系统、人工设计多智能体系统及既有进化方法；进一步分析表明，Meta-Team能实现更可靠、更具可扩展性的多智能体系统自我进化。