Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches typically expand these banks in an append-only fashion, continuously adding new skills without removing redundant, outdated, or harmful ones, resulting in inefficient and poorly curated repositories. In this paper, we formulate the skill bank curation as a constrained multi-objective problem: a desirable bank must be useful for the agent, diverse in its content, and provide good coverage of the query distribution. To this end, we introduce SkillBrew, a multi-objective curation framework that formalizes skill bank curation as Pareto-aware optimization under a utility constraint, and solves it via a bi-level propose-then-verify loop. We evaluate our approach on two public benchmarks. Our findings suggest that treating skill banks as objects of principled curation, rather than ever-growing append-only logs, is an important step toward building self-improving LLM agents.

翻译：检索增强型大语言模型智能体日益依赖经过策展的技能库——这些包含可复用文本规则的集合能指导复杂任务的决策。现有方法通常采用仅追加的扩展方式，持续新增技能却未移除冗余、过时或有害内容，导致存储库效率低下且缺乏良好组织。本文将对技能库的策展形式化为带约束的多目标优化问题：理想库需兼具对智能体的有用性、内容多样性及对查询分布的充分覆盖。为此，我们提出SkillBrew多目标策展框架，该框架将技能库策展建模为带效用约束的帕累托感知优化，并通过双层"提出-验证"循环求解。我们在两个公开基准上评估该方法。研究结果表明，将技能库视为需遵循策展原则的对象，而非无限增长的追加记录，是构建自我改进型LLM智能体的关键步骤。