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English(EN) EvolveMAS: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems

新框架CEPO和EvolveMAS增强多智能体系统

作者 PulseAugur 编辑部 · [4 个来源] ·

两篇新研究论文介绍了用于增强多智能体系统（MAS）的框架。CEPO（协调员-执行者提示优化）将系统级协调与智能体级执行分离，从而在各种领域中改进提示优化。EvolveMAS为去中心化MAS提供了一个统一的自进化框架，使智能体能够在测试时进行适应，并在不更新参数的情况下跨回合演化知识。两种方法都在各自的基准测试中展示了显著的性能提升。 AI

影响 这些框架可能导致更复杂和自适应的多智能体系统，从而改善复杂AI交互中的协调和学习。

排序理由 arXiv上发表的两篇研究论文，介绍了多智能体系统的新框架。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新框架CEPO和EvolveMAS增强多智能体系统

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arXiv上发表的两篇研究论文，介绍了多智能体系统的新框架。
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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Jongha Kim, Seohyun Lee, Jonathan Minho Chung, Jinsung Yoon, Hyunwoo J. Kim ·

    CEPO：一个用于改进多智能体系统中提示优化的通用框架

    arXiv:2610.34722v1 Announce Type: new Abstract: Multi-agent systems (MAS) require both effective coordination and specialized task execution. Existing prompt optimization approaches typically couple these functions within agent-specific prompts. We introduce Coordinator-Executor …

  2. arXiv cs.AI TIER_1 English(EN) · Yalun Wu, Qiwei Ma, Bingbing Xu, Dan Zhang, Long Bai, Boyang Wang, Jiatong Li, Zheqi Lv, Shaozu Yuan, Peiying Wang, Jiawei Wang, An Zhang, Tat-Seng Chua ·

    EvolveMAS：去中心化多智能体系统的统一自进化框架

    arXiv:2610.34450v1 Announce Type: cross Abstract: Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other agents at test time and reuse accumulated experience to refine their interaction str…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Tat-Seng Chua ·

    EvolveMAS：去中心化多智能体系统的统一自进化框架

    Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other agents at test time and reuse accumulated experience to refine their interaction strategies for future interactions across cooperative…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    EvolveMAS：去中心化多智能体系统的统一自进化框架

    Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other agents at test time and reuse accumulated experience to refine their interaction strategies for future interactions across cooperative…

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