Sakana AI's MASS has one model design, run, and judge its own agent teams, then train on the best runs

A single shared model plays executor, workflow optimizer, and evaluator, searches for better multi-agent workflows, and fine-tunes on executions of the team it judged best. After two cycles with Qwen3.6-27B, score per output token reached 1.2 to 1.6x the base model on four research benchmarks, and the authors found the optimizer, not the evaluator, was the bottleneck; paper and code are open.