Why ML research agents don't overfit: compression theory explains

Why don't machine learning research agents overfit?

Amazon Science explains why machine learning research agents rarely overfit when auto-tuning or searching architectures. The key idea: these agents effectively compress training data—the better the compression, the better the generalization. It applies the classic 'compression equals intelligence' hypothesis to the search process itself. The post offers a theoretical perspective without specific experimental numbers or baselines.