English(EN) AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

AgentGrad框架通过新的干预方法优化多智能体LLM提示

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了AgentGrad，一个用于优化大型语言模型驱动的多智能体系统提示的新框架。该方法通过实现顺序干预来解决现有文本梯度方法的局限性，以查明导致失败的特定智能体，并提取具有智能体级别监督的细粒度梯度。AgentGrad还使用语义文本梯度抽象来对相似梯度进行分组，从而实现更通用、更有效的提示更新。实验表明，AgentGrad在五个基准测试中取得了最先进的性能，并显著减少了优化时间。 AI

影响这个新的提示优化框架可能带来更高效、更有效的多智能体AI系统，从而可能提高各种AI应用的性能。

排序理由该集群包含一篇详细介绍AI系统提示优化新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jaewon Chu, Jinwoo Seo, Jaewon Cho, Jeehye Na, Yunyang Xiong, Youngdae Kim, Hyunwoo J. Kim · 2026-09-10 04:00

AgentGrad：面向多智能体系统的干预引导提示优化

arXiv:2609.08572v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual g…

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AgentGrad：面向多智能体系统的干预引导提示优化

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