Compositional Recursive Self-Improvement

Recursive self-improvement is becoming an important direction for building agents that can learn from their own experience and improve their future behavior. Yet current RSI systems do not clearly expose which parts of a successful harness should be retained, how compatible discoveries from different executions should be combined, or how the value of one control changes with its collaborators. Whole-harness updates couple these decisions and make reusable improvement units difficult to preserve. We address this gap with , a compositional harness-adaptation method for RSI systems. retains typed harness modules while evaluating the complete assembled harness: base inheritance preserves unchanged controls, guarded assembly combines compatible discoveries, and cooperation memory informs later task-conditioned composition. Across multiple domains, datasets, and baseline systems, improves transfer performance and rubric-scored analysis quality while producing traceable workflow and artifact evidence. The method provides a compositional interface for retaining and reusing control improvements in recursive self-improvement systems.