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About this paper
Kunfeng Chen, Qihuang Zhong, Juhua Liu, Bo Du
SkillCAT improves reusable instructions for language-model agents by learning from execution histories and selecting skill content for new tasks. It compares successful and failed runs of the same task to propose patches, replays patches on their source tasks, and routes relevant skill components at deployment. On the reported aggregate, it scores 59.04% versus EvoSkill’s 42.21%, not winning every comparison.
Key insights
- Comparing successful and failed execution traces is crucial for extracting high-quality, causally relevant evidence for skill refinement, leading to more robust skill edits.
- A rigorous validation step, such as assessing patch impact on task outcomes before integration, is essential to prevent the accumulation of detrimental or ineffective skill content.
- Dynamically assembling a compact, task-relevant skill context by selecting specific skill components significantly improves inference efficiency and agent performance, especially with large skill corpora.
Summary by alphaXiv.
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