English(EN) Beyond Top-$k$ Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents

LLM 代理获得面向多样性的技能路由框架

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

研究人员推出了一种名为 Diverse Skill Routing (DSR) 的新框架，旨在改进大型语言模型 (LLM) 代理选择和利用外部技能的方式。与仅根据查询相关性对技能进行排名的现有方法不同，DSR 采用行列式点过程来平衡相关性与技能互补性。这种方法旨在避免在冗余技能上浪费上下文，并为复杂任务更好地选择互补技能。DSR 在 SkillRouter 基准测试中展示了改进的召回率和覆盖率，尤其是在需要多种技能的查询方面。 AI

影响通过改进复杂任务的互补技能选择来增强 LLM 代理的能力。

排序理由该集群包含一篇详细介绍 LLM 代理新方法的学术论文。[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 →

本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Wang Wei, Tiankai Yang, Samyadeep Basu, Hongjie Chen, Yue Zhao, Zhengzhong Tu, Xiyang Hu, Franck Dernoncourt, Ryan A. Rossi, Hoda Eldardiry · 2026-09-10 04:00

超越 Top-$k$ 技能检索：LLM 智能体的多样性感知技能路由

arXiv:2609.05824v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redundant while complex tasks often require complementar…

本文如何被排名

报道来源 [1]

超越 Top-$k$ 技能检索：LLM 智能体的多样性感知技能路由

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