Recursive Self-Improvement through Collective Intelligence

As AI systems tackle increasingly open-ended research problems, human experts may struggle to assess results and guide further progress. The model itself may be the best available optimizer and evaluator. The challenge is obtaining useful self-supervision within that homogeneous loop, where a model risks reinforcing its own blind spots rather than correcting them. We ask whether a model can learn from the collective intelligence of its own team. Introducing our new paper, Recursive Self-Improvement through Multi-Agent Self-Supervision: Detailed Blog: https://pub.sakana.ai/mass/ Paper: https://