Counterfactual Evolution Credit: Causal Selection for Self-Evolving Agent Skills

Self-evolving language-model agents convert execution experience into persistent updates to reusable skills. A central challenge is determining which candidate skills to retain. Existing selection strategies typically assign credit based on episode-level outcomes or invocation audits, conflating skill availability, invocation, behavioral influence, and causal contribution to task success. As a result, a candidate may be retained because it co-occurs with easy instances or discarded because unrelated execution noise masks its utility. We introduce Counterfactual Evolution Credit (CEC), a causal selection framework that estimates the incremental contribution of structured skill artifacts through matched execution interventions. CEC compares executions using a candidate skill against counterfactual runs in which the skill is removed, replaced by its parent, blocked from invocation, or modified at a specific functional module. These comparisons control for the task instance, environment state, execution budget, and, when possible, randomness. Repeated paired evaluations with fresh agents quantify residual uncertainty, while sequential allocation assigns additional evaluations to promising candidates whose effects remain unresolved. CEC also supports hierarchical attribution at the levels of whole skills, triggers, procedures, and recovery logic, allowing beneficial revisions to be retained and harmful components to be localized. We evaluate CEC under controlled failure modes involving unused, behaviorally inert, and spuriously correlated skills. The evaluation includes matched-budget comparisons, cross-agent reproduction, and ablations of replay control, confidence estimation, attribution granularity, and budget allocation. CEC supports skill selection under stochastic execution trajectories and limited evaluation budgets by separating causal contribution from invocation and episode-level correlation.