Beyond the Best Skill: Router–Expert Coevolution for Adaptive Skill Trees

Large language model (LLM) agents can improve by revising reusable textual skills without updating model weights. However, continued single-skill search can plateau despite useful local repairs. Our analysis identifies structural interference: repairs on some inputs are offset by regressions on others when candidates are evaluated as global replacements. We introduce RECAST (Router–Expert Coevolution for Adaptive Skill Trees), which expands an optimized Root skill into an adaptive skill tree by jointly evolving Expert skills and their routing scopes. Residual errors guide specialization, and Experts are refined through repair and harm evaluations relative to their parents on routed examples. Router selection uses end-to-end validation performance with the current Experts, while the selected Router defines the scope for further Expert refinement. Validation guides acceptance and recursive growth. At inference, the frozen tree executes the Root and follows a single routed path, returning the current node's output when routing stops. Across six benchmarks, two Qwen models, and Roots from GEPA and SkillOpt, RECAST improves all 24 paired configurations, with six-task macro-average gains of 12.82 to 15.69 percentage points. On three tasks with GPT-5.4, the macro-average gain is 15.09 percentage points. Ablations support joint adaptation of Routers and Experts, and depth analysis shows further gains from recursive specialization. Across six workflow comparisons, an upstream optimization prefix followed by RECAST achieves higher test performance with fewer optimization and validation tokens than completing upstream single-skill search.