Sakana AI's self-improving model is stronger at solving and judging, yet cannot catch its own blind spots

Researchers at Sakana AI and UC Berkeley proposed a multi-agent self-supervision method called MASS, in which the same language model simultaneously takes on three roles—solving problems, optimizing workflows, and judging results—to achieve recursive self-improvement without a stronger teacher model. The base model is Qwen3.6-27B, and the tasks are 12 financial, robotics, and pharmaceutical research tasks generated by GPT-5.5. On the test tasks, the win rate against the base model rose from 53.9% after one round to 69.9% after two rounds; on MLR-Bench, DSBench, ScienceAgentBench, and AstaBench, the score per output token was 1.2 to 1.6 times that of the base model, but on Terminal-Bench 2.0 and SWE-bench Verified the scores dropped slightly; the agreement rate between the self-evaluator and external judges rose from 73% to 93%. The code has been released.