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New frameworks CEPO and EvolveMAS enhance multi-agent systems

By PulseAugur Editorial · [4 sources] ·

Two new research papers introduce frameworks for enhancing multi-agent systems (MAS). CEPO (Coordinator-Executor Prompt Optimization) separates system-level coordination from agent-level execution, improving prompt optimization across various domains. EvolveMAS offers a unified self-evolution framework for decentralized MAS, enabling agents to adapt at test time and evolve knowledge across episodes without parameter updates. Both approaches demonstrate significant performance gains in their respective benchmarks. AI

IMPACT These frameworks could lead to more sophisticated and adaptive multi-agent systems, improving coordination and learning in complex AI interactions.

RANK_REASON Two research papers published on arXiv introducing new frameworks for multi-agent systems.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New frameworks CEPO and EvolveMAS enhance multi-agent systems

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COVERAGE [4]

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

    CEPO: A General Framework for Improving Prompt Optimization in Multi-Agent Systems

    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: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems

    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: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems

    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: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems

    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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