Sakana AI Model Has Shared Blind Spots

Sakana AI researchers introduce Multi-Agent Self-Supervised learning, a method where a single model improves itself by solving tasks and judging results. While win rates rise to 69.9 percent after two rounds, the system risks reinforcing errors because the internal evaluator sometimes accepts flawed steps.