SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion

2026-08-15

AI Summary

This study addresses the issue of erroneous policy merging caused by semantic similarity during experience integration in LLM agents. To mitigate this, we propose an Online Skill Evolution framework that abstracts experiences into a hierarchical, reusable skill library through behavior-verified scope expansion, cross-instance replay, and mechanism validation. This approach effectively prevents semantic misguidance while ensuring behavioral consistency. Experimental results demonstrate that the framework consistently enhances agent performance across multiple benchmarks. Furthermore, the acquired skills exhibit transferability across different model scales and families, achieving continuous learning and generalization without compromising behavioral fidelity.

Abstract

Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge. However, existing methods often consolidate experience based on semantic similarity or LLM judgments, which may merge superficially related but behaviorally incompatible strategies and thereby degrade performance. To address the issue, we propose SkillCommit, an online skill evolution framework that continuously transforms experience into a hierarchical library of reusable skills. Each new experience is initially preserved as an instance-specific patch, retaining the behavior validated in its local context. As related skills accumulate, SkillCommit abstracts those sharing a common behavioral mechanism into higher-level skills. Specifically, for each incoming skill, embedding-based retrieval first identifies candidate related skills. Cross-instance replay and an LLM-based mechanism check determine whether these skills transfer across cases and share a common underlying mechanism. Candidates that pass both checks are abstracted into a higher-level skill and committed only if it preserves the validated behavior of all constituent skills. Experiments on RuleArena, OpenExempt and KOR-Bench demonstrate that SkillCommit consistently improves agent performance across diverse domains. Moreover, the learned skills transfer across model scales and families, enabling cross-model experience transfer.

Problem

• LLM agents
• skill evolution
• experience consolidation
• behavioral compatibility
• reusable skills

Innovation

• Behaviorally Validated Scope Expansion
• Hierarchical Skill Library
• Cross-instance Replay
• Online Skill Evolution
• Cross-model Transfer