MICA: Harness Self-Evolution through Intervention-Guided Co-Adaptation

Harness self-evolution refines runtime instructions through persistent context edits, but individually beneficial edits can fail in combination because one edit can change how another is interpreted or executed. We introduce MICA (Matched Intervention-guided Co-Adaptation), which evolves the harness by jointly adapting edit structures and templates while keeping language models frozen. Matched executions guide a frozen language model to propose composition-mismatch hypotheses and minimal template repairs. With edit structure fixed, MICA compares the original template pair, each unilateral revision, and the joint revision to measure the benefit of co-adaptation. Competing structures receive the same bounded adaptation allowance before execution-based selection, followed by frozen screening and independent deployment confirmation. Across five environments, MICA achieves macro-average scores of 80.90% with GPT-5.5 and 76.80% with DeepSeek-V4-Flash, exceeding the strongest baselines by 4.82 and 1.72 percentage points, respectively. Under common budgets, MICA improves deployed scores by 3.25 percentage points on Telecom and 4.23 percentage points on AppWorld Normal over the strongest search baseline. Controlled studies demonstrate that matched evidence and paired repair each improve deployment reward, establishing harness co-adaptation as an effective approach to harness self-evolution.