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English(EN) EMMA: Structured Episodic Memory for Multi-Agent Workflow Synthesis

EMMA系统通过结构化记忆增强多智能体工作流合成

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出EMMA，一种使用结构化情景记忆合成多智能体工作流的新系统。EMMA通过明确更新所有权和可采纳性，解决了当前LLM代理部署的局限性，从而实现更可控和有效的学习。在KramaBench、AppWorld和BBEH基准测试中进行测试，EMMA表现出色，性能优于基线高达32.5%，并且使用的训练执行次数更少。 AI

影响 这项研究可能带来更高效、更适应复杂多步任务的LLM代理。

排序理由 该集群描述了一篇关于用于多智能体工作流合成的新颖系统的研究论文。

在 arXiv cs.AI 阅读 →

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EMMA系统通过结构化记忆增强多智能体工作流合成

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ruijia Niu, Kaiwen Liu, Rose Yu, Kristina Klinkner ·

    EMMA：用于多智能体工作流合成的结构化情景记忆

    arXiv:2610.35966v1 Announce Type: cross Abstract: Deploying LLM agents in specialized domains often require fine-grained task-specific procedures and knowledge a general model lacks, yet parameter training is costly and non-interpretable, and hand-engineered agent systems shift t…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Kristina Klinkner ·

    EMMA：面向多智能体工作流合成的结构化情景记忆

    Deploying LLM agents in specialized domains often require fine-grained task-specific procedures and knowledge a general model lacks, yet parameter training is costly and non-interpretable, and hand-engineered agent systems shift the cost to expert labor. Episodic memory updates a…

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