Reducing cross-sample prediction churn in scientific machine learning

Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training data. Across 9 chemistry benchmarks, two classifiers trained on independent bootstraps of the same training set agree on aggregate accuracy to within 1.3–4.2 percentage points but disagree on the class label of 8.0–21.8% of test molecules. We call this gap cross-sample prediction churn. The standard parameter-side techniques (deep ensembles, MC dropout, stochastic weight averaging) do not reduce this gap; two data-side methods do. The first is K-bootstrap bagging, which cuts the rate 40–54% on every dataset at no accuracy cost (K×-ERM compute). The second is twin bootstrap, our proposal: two networks trained jointly on independent bootstraps with a sym-KL consistency loss between their predictions, which at matched 2×-ERM compute reduces churn a further median 45% beyond bagging-K=2. Cross-sample prediction churn deserves a column alongside predictive performance in scientific-ML benchmark reports, because without it the parameter-side and data-side methods are indistinguishable on the metric they actually differ on.