GoKawiilStudy probes why AI research agents resist overfitting despite iterative benchmark tuning
The piece examines a paradox in machine learning practice: researchers repeatedly check performance on held-out validation data while iterating on model design, a process that in theory should contaminate that data and cause overfitting. Yet in practice, research agents and human researchers alike often generalize well despite this repeated peeking, prompting a deeper look at why the standard overfitting warning doesn't always play out as expected.