Sakana AI and UC Berkeley propose MASS self-improvement method; capabilities improve but shared blind spots may perpetuate errors
Researchers from Sakana AI and UC Berkeley proposed the multi-agent self-supervised method MASS, enabling a single Qwen3.6-27B model to simultaneously solve problems, evaluate results, and optimize outputs, allowing recursive self-improvement without requiring a stronger teacher model.
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