Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910270084874240 |
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| author | Hao, Zhezheng Wang, Tianfu Dong, Huanshuo Liu, Ziyan Wang, Hong Lin, Xiankun Lin, Qiang Wang, Can Dong, Hande Chen, Jiawei |
| author_facet | Hao, Zhezheng Wang, Tianfu Dong, Huanshuo Liu, Ziyan Wang, Hong Lin, Xiankun Lin, Qiang Wang, Can Dong, Hande Chen, Jiawei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29790 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems Hao, Zhezheng Wang, Tianfu Dong, Huanshuo Liu, Ziyan Wang, Hong Lin, Xiankun Lin, Qiang Wang, Can Dong, Hande Chen, Jiawei Multiagent Systems Artificial Intelligence 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. |
| title | Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2605.29790 |