Sakana AI Tests Recursive Self-Improvement

Sakana AI researchers launch Multi-Agent Self-Supervision to advance frontier models. Starting from Qwen3.6-27B, the system runs two rounds on twelve synthetic tasks. Win rates against the base model rise from 53.9% to 69.9%, while scores per token reach 1.2 to 1.6 times the baseline across public benchmarks.