AI 中文总结
针对任务查询与技能间的查询-技能不匹配问题，提出SkillDreamer框架，通过推断任务所需能力、生成伪技能的前瞻信息，提升LLM智能体的技能检索与任务执行效果。
AI 中文摘要
技能检索是近期兴起的一种有前景的范式，用于从技能库中识别合适的执行指南，从而为大语言模型（LLM）智能体提供完成指定任务所需的程序性知识。为此，现有大多数方法会定制检索模型或重新配置检索流程，以优先选择与任务查询语义最相关的技能。然而，我们通过实验发现，任务查询与技能自然分别从目标导向和程序导向的不同视角构建，这导致了一个未被充分探索的问题，即查询-技能不匹配（Query--Skill Misalignment，QSM）。显然，在QSM的背景下关联合适的技能是艰巨甚至不可能的，这会阻碍智能体正确执行任务。针对这一问题，受人类前瞻认知的启发，我们提出了SkillDreamer这一新颖框架，以缓解QSM问题的负面影响。简言之，SkillDreamer首先推断任务执行所需的能力，然后通过生成伪技能来想象如何实现这些能力，最后利用这类前瞻信息弥合目标导向的任务查询与执行导向的技能之间的差距。在SkillRet和SkillUsage上开展的大量实验，不仅验证了SkillDreamer在技能检索和端到端任务执行两方面的有效性，还证明了其在不同检索模型和流程间的泛化能力。代码将在论文接收后发布。
英文摘要
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.
