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English(EN) M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement

M-Plan框架通过集成长期和短期记忆来增强LLM代理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的闭环多智能体系统（MAS）框架M-Plan，旨在提高LLM代理在长周期和多轮任务上的性能。M-Plan集成了长期和短期记忆，并采用全局-局部和过去-当前指导策略。这种方法允许代理在需要时接收任务特定的里程碑和局部提示，同时还优化和重组信息以供将来检索到长期记忆中。在ALFWorld和Webshop等任务上的实验表明，M-Plan在准确性和效率方面优于基线方法。 AI

影响 该框架可以提高LLM代理在复杂、多轮任务中的效率和准确性。

排序理由 该集群描述了一篇关于LLM代理新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

M-Plan框架通过集成长期和短期记忆来增强LLM代理

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该集群描述了一篇关于LLM代理新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ziyang Liu, Jiakang Yuan, Jiayuan Fan, Tao Chen ·

    M-Plan：LLM智能体记忆编排的综合规划

    arXiv:2610.27032v1 Announce Type: new Abstract: Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, particularly in environments without prior training. A key limit…

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