PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909752586403840 |
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| author | Deng, Yingfan Zhou, Anhao Yuan, Yuan Zhang, Xiao Zou, Yifei Yu, Dongxiao |
| author_facet | Deng, Yingfan Zhou, Anhao Yuan, Yuan Zhang, Xiao Zou, Yifei Yu, Dongxiao |
| contents | Multi-Agent Systems have recently emerged as a promising paradigm for collaborative reasoning and solving complex tasks. However, the design of collaborative learning algorithms in multi-agent systems faces several challenges, including high communication overhead and insufficient agent-level personalization. In this paper, we propose PE-MA (Parameter-Efficient Multi-Agent Co-Evolution), a novel collaboration framework that supports efficient, scalable, and personalized co-evolution in multi-agent systems. In PE-MA, each agent maintains a lightweight personalized adapter to support agent-specific behavior, while a shared adapter is collaboratively optimized across neighboring agents. This design balances global coordination with local adaptation under heterogeneous environments. We achieve an asymptotically optimal convergence rate of O( 1/(NK)^(1/2) ), where N is the number of agents and K the local update steps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems Deng, Yingfan Zhou, Anhao Yuan, Yuan Zhang, Xiao Zou, Yifei Yu, Dongxiao Multiagent Systems Multi-Agent Systems have recently emerged as a promising paradigm for collaborative reasoning and solving complex tasks. However, the design of collaborative learning algorithms in multi-agent systems faces several challenges, including high communication overhead and insufficient agent-level personalization. In this paper, we propose PE-MA (Parameter-Efficient Multi-Agent Co-Evolution), a novel collaboration framework that supports efficient, scalable, and personalized co-evolution in multi-agent systems. In PE-MA, each agent maintains a lightweight personalized adapter to support agent-specific behavior, while a shared adapter is collaboratively optimized across neighboring agents. This design balances global coordination with local adaptation under heterogeneous environments. We achieve an asymptotically optimal convergence rate of O( 1/(NK)^(1/2) ), where N is the number of agents and K the local update steps. |
| title | PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2506.11803 |