CtrlSkill: A Control-Theoretic View of Controllable Skill Self-Evolution

Large language model agents can distill reusable skills from execution trajectories, yet accumulating experience does not necessarily yield reliable performance gains. Success on local validation is insufficient to justify persistent skill adoption, failures with mixed causes can lead to spurious generalization, and tool execution errors may be misattributed to reasoning deficiencies. We introduce CtrlSkill, a Control-Theoretic View of Controllable Skill Self-Evolution that organizes updates to an external skill policy as an evidence-driven feedback loop. CtrlSkill first constructs structured evidence to distinguish intended actions, tool execution, and observed outcomes. It then converts shared evidence gaps into candidate skills with explicit applicability conditions and verification requirements. Finally, it compares the active and candidate policies on subsequent matching tasks, controlling whether updates take effect through acceptance, deferral, or rejection. By decoupling candidate generation from policy commitment, CtrlSkill subjects skill updates to explicit constraints on supporting evidence, edit scope, and validation feedback. Across these settings, CtrlSkill raises the overall average score over baselines by up to 23%.