生成式技能组合：为LLM智能体实现结构化技能规划

Computer Science > Computation and Language

arXiv:2606.32025v1 (cs) [Submitted on 30 Jun 2026]

Title:Generative Skill Composition for LLM Agents

Authors:Xinyu Zhao, Zhen Tan, Vaishnav Tadiparthi, Nakul Agarwal, Kwonjoon Lee, Ehsan Moradi Pari, Hossein Nourkhiz Mahjoub, Tianlong Chen View a PDF of the paper titled Generative Skill Composition for LLM Agents, by Xinyu Zhao and 7 other authors View PDF HTML (experimental)

Abstract:Recent LLM agents benefit from skills for solving complex tasks. Skills encapsulate modular packages of procedural knowledge and instructions for performing specialized tasks, such as setting up a sandboxed environment, running a test suite, or refactoring a function across multiple files. As skill libraries grow and become reusable across tasks and domains, selecting an appropriate skill composition has emerged as a central bottleneck. Existing approaches fall into two categories. One exposes the agent's reasoning to the entire skill collection; the other performs skill retrieval via embeddings or LLM-based rerankers. Both provide useful insights; however, they miss the structural nature of skill composition, which is a joint decision over which skills, how many, and in what order -- three dimensions that cannot be decoupled. We formalize this as structured skill composition: given a task and a skill library, predict an executable skill plan that jointly specifies the activated subset, count, and execution order. We propose SkillComposer, which instantiates structured skill composition as task-conditioned skill sequence prediction. SkillComposer uses a constrained autoregressive decoder over skill identifiers, so subset, count, and order emerge jointly from a single decoding pass, and dependencies between successive skills are captured naturally. We build a training set of task-composition pairs from a real, human-curated skill library. We then evaluate SkillComposer along two axes: composition quality on a held-out test set, and downstream task success on SkillsBench across two production-grade coding agents. On GPT-5.2-Codex, Gemini-3-Pro-Preview, SkillComposer raises the pass rate by +23.1, +18.2pp over the no-skill baseline, surpassing top-3 retrieval and matching the gold-skill retrieval upper bound at lower prompt-token cost.

Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2606.32025 [cs.CL]
	(or arXiv:2606.32025v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2606.32025 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Xinyu Zhao [view email]
[v1] Tue, 30 Jun 2026 17:53:09 UTC (666 KB)
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