English(EN) MAS-Loop: Beyond One-Shot Orchestration towards Self-Evolving Multi-Agent Systems

MAS-Loop框架通过迭代优化提升多智能体系统训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 21:32

研究人员推出了MAS-Loop，一个旨在通过超越单次编排来改进多智能体系统（MAS）训练的新型框架。与仅从系统最终结果中学习的先前方法不同，MAS-Loop将一个编排器与一个优化器相结合。该优化器分析执行轨迹以识别失败、诊断问题并提出对智能体或其配置的修改。然后重新执行更新后的系统，为编排器生成更好的初始系统和优化器改进其修复能力提供反馈，从而实现共同进化和更强的整体性能。 AI

影响该框架通过在训练过程中实现迭代学习和自我纠正，可能带来更强大和更具适应性的多智能体系统。

排序理由该集群包含一篇详细介绍多智能体系统新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Chengwei Qin · 2026-10-28 21:32

MAS-Loop：超越单次编排，迈向自演化多智能体系统

Training-time multi-agent system (MAS) design seeks to learn reusable orchestration policies, yet prevailing approaches remain largely one-shot: an Orchestrator generates a system but only observes and learns from its terminal outcome. This paradigm discards execution trajectorie…

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MAS-Loop：超越单次编排，迈向自演化多智能体系统

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