Sakana AI's MASS: Division of Labor Among Self-Improving AI Agents Avoids Reinforcing Errors
hardmaru · x · 2026-10-13
Sakana AI introduces MASS (Multi-Agent Self-Supervision), a new approach to recursive self-improvement through collective intelligence. The core problem: as AI tackles increasingly open-ended research problems, human experts may struggle to assess results, making the model itself the best available optimizer and evaluator — but a homogeneous self-supervision loop risks reinforcing the model's own blind spots. MASS instead lets a single shared model solve tasks via a team of virtual agents, using division of labor to provide useful self-supervision and avoid self-reinforcing errors. hardmaru finds it compelling from a metascience perspective: science is an open-ended search with no explicit benchmark, and the scientific community bootstraps itself through collective work rather than rigid metrics — mirroring MASS's design.
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