Frontis-MA1 and OpenMLE enable recursive ML self-improvement

Recursive self-improvement in specialized AI agents shifts machine learning automation from static script generation to dynamic evolutionary development. Training around explicit operators (Draft, Improve, Debug, Crossover) establishes a structured framework for targeted iterative code optimization. Open-sourcing the full OpenMLE stack provides researchers with an accessible testbed for benchmarking long-horizon self-improvement loops. As agentic self-modification scales, maintaining alignment and avoiding local minima in synthetic code generation will be the critical evaluation metric.