Imagine Before Retrieval: Prospective Skill Retrieval for LLM Agents

2026-08-28

AI Summary

研究针对任务查询与技能之间的错位问题，提出SkillDreamer框架，通过生成伪技能来弥合目标导向查询和执行导向技能间的差距，提高大型语言模型的任务执行能力。

Abstract

Skill retrieval has recently emerged as a promising paradigm for identifying the desirable execution guidelines from the skill gallery, thus equipping large language model (LLM) agents with the procedural knowledge to accomplish the specified task. To this end, most existing methods customize the retrieval model or reconfigure the retrieval pipeline to prioritize skills that are most semantically relevant to the task query. However, we empirically reveal that task queries and skills are naturally formulated from different perspectives, namely, objective-oriented and procedural-oriented, leading to an under-explored problem termed Query--Skill Misalignment (QSM). Clearly, it is daunting and even impossible to associate the desirable skills in the context of QSM, thus hindering the agent from correctly executing the task. As a remedy, inspired by human prospective cognition, we propose SkillDreamer, a novel framework to alleviate the negative impact of QSM problem. In brief, SkillDreamer first infers the capabilities necessary for task execution, then imagines how to realize these capabilities by generating pseudo skills, and finally leverages such prospective information to bridge the gap between objective-oriented task queries and execution-oriented skills. Extensive experiments on SkillRet and SkillUsage not only verify the effectiveness of SkillDreamer in both skill retrieval and end-to-end task execution, but also demonstrate its generalizability across diverse retrieval models and pipelines. The code will be released upon acceptance.

Problem

• Skill Retrieval
• Query--Skill Misalignment
• Large Language Model

Innovation

• SkillDreamer
• Query-Skill Misalignment (QSM)
• prospective cognition
• pseudo skills
• skill retrieval