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Sakana AI's Self-Improving Model Gains Benchmarks but Misses Its Own Blind Spots

Researchers at Sakana AI and UC Berkeley introduced MASS, a method letting one language model recursively improve itself. Win rates reach 69.9 percent after two rounds, but shared blind spots can compound errors across rounds.