Nature Machine Intelligence, Published online: 09 April 2026; doi:10.1038/s42256-026-01205-z
Neural networks may be overconfident before they see real data. By briefly training on random noise, models can learn to be uncertain, leading to better calibration, improved identification of out-of-distribution inputs and thus more reliable predictions.