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arXiv Machine Learning By Huancheng Chen, Xiaodi Sun, Zhaoqiong Huang, Shenyang Huang Shreya Singhal, Jingwen Lu

SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization

Read the original on arXiv Machine Learning →

SkillSpec is a two‑phase framework for evolving natural‑language skills in large language model agents. The first phase, consensus‑gated evolution, generates candidate skills from complementary editing intents and commits updates only when paired evaluations reach consensus on overall improvement and non‑negative aggregate gain. The second phase, representation specialization, uses signals from the optimization trajectory to choose an appropriate flat, graph, or hybrid structure for the skill, improving success rates by an average of 6.89% over SkillOpt across six benchmarks and three target language models.

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