DiagEvo is a self-evolution framework that derives its training curriculum entirely from information produced during self-play rather than external task resources. A diagnostician component extracts recurring error causes from a solver’s failure history and stores them in hierarchical memory tracking skill mastery, while a challenger generates targeted questions addressing unresolved weaknesses. The approach improves performance across nine benchmarks and three solver models in the authors’ experiments.
