GSME

Self-evolving agent harness framework uses quality-diversity archive to generalize harness improvements across model families

A new arXiv preprint introduces a framework for automatically improving LLM agent harnesses (prompts, injected knowledge, runtime control, configuration) without modifying model weights. The system separates change proposal (done by an LLM diagnosing failures) from credit assignment (done by deterministic statistical testing), populating a gated quality-diversity archive keyed on failure pathology rather than task identity to prevent overfitting. Evaluated across seven domains with a frozen open-weight model, the framework achieves +9 to +15.5 percentage point gains on sealed test sets, retaining 86–147% of training gains, with the key finding that pathology-to-patch mappings transfer across model families.