MASS achieves recursive self-improvement via multi-agent self-supervision

MASS uses multi-agent workflows to generate self-supervision signals, distilling collective intelligence to improve base model performance on research benchmarks. The method optimizes the model's score per output token by iteratively treating the model as both optimizer and evaluator.