CoEvo-RSI: A Recursive Self-Improvement Framework for Long-Horizon Model–Harness Co-Evolution

LLM-based agents now often couple a foundation model with an execution harness that determines what the model observes and how its actions are carried out. Recent work has begun enabling agents to self-improve their own harnesses. However, evolving the harness alone can create a growing mismatch between the model and its execution interface. Recursive co-evolution addresses this asymmetry by updating both components iteratively, yet existing approaches struggle over long horizons in that feeding every trajectory to both updates introduces excessive noise and can cause collapse, while hand-crafted attribution rules become misaligned as the system evolves and cause performance to saturate. We introduce CoEvo-RSI, a Recursive Self-Improvement framework proposed for long-horizon model–harness co-evolution. CoEvo-RSI scores each task for a receiving component based on the measured effect of the counterpart's latest update and a failure attribution weighted by the outcomes of prior interventions. Across 40 rounds on four benchmarks, CoEvo-RSI continues to improve as several published allocation strategies plateau or decline, and consistently outperforms existing co-evolution and RSI methods, achieving state-of-the-art performance in recursive model–harness co-evolution.