EvoMoSkill: Evolving a Mixture-of-Skills for LLM Agents

Enabling agent skills to autonomously refine reusable knowledge is a central challenge in adapting large language model (LLM) agents to real-world applications. Recent work treats skills as trainable external parameters, following the iterative optimization paradigm of deep learning. However, these methods typically optimize a single skill across tasks, so revisions that benefit one task group can disrupt others. A skill library can limit this interference, but raises questions across skill creation, task routing, failure attribution, and evaluation. We introduce EvoMoSkill, a mixture-of-skills framework that jointly optimizes a share skill, expert skills, and a router. Each optimization step applies Credit-Assigned Gradient Descent (CreAGD) in five stages: (1) **Forward execution and credit assignment** attributes execution failures to skill text or router scope; (2) **Textual and routing gradient descent** edits skills, creates experts, and revises routing; (3) **Scope-conditioned paired evaluation** compares candidates with the original system on tasks they would serve; (4) **History-calibrated step acceptance** checks gains against past outcomes and accepts revisions after validation-set checks; and (5) **Optimizer memory accumulation** uses rejected drafts and feedback to improve later updates. Experiments with five LLMs on five benchmarks demonstrate state-of-the-art performance, substantially outperforming human-authored skills, LLM-generated skills, and baselines that optimize only a single skill. These results show that evolving mixture-of-skills yields more adaptable agents.