Recursive Self-Improvement through Multi-Agent Self-Supervision

Published 17 October 2026

A method called MASS lets one AI model improve itself by designing and evaluating multi-agent workflows, then training on the generated reasoning traces. In tests, two improvement cycles raised performance per output token by 1.2–1.6× on four open-ended benchmarks, while multi-agent traces helped students learn with fewer tokens.