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 $1.3\text{--}4.2$ percentage points but disagree on the class label of $8.0\text{--}21.8\%$ of test molecules. We call this gap \emph{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 $40\text{--}54\%$ on every dataset at no accuracy cost ($K{\times}$-ERM compute). The second is \emph{twin-bootstrap}, our proposal: two networks trained jointly on independent bootstraps with a sym-KL consistency loss between their predictions, which at matched $2{\times}$-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.

翻译：科学机器学习报告预测性能，但未说明同一预测结果是否会在不同训练数据抽取中保持不变。在9个化学基准测试中，基于同一训练集独立自助法采样训练的两个分类器，其聚合精度差异在1.3–4.2个百分点以内，但对8.0–21.8%的测试分子类别标签存在分歧。我们将此差距称为“跨样本预测波动”。标准参数侧技术（深度集成、MC dropout、随机权重平均）无法缩小此差距，而两种数据侧方法有效：第一种是K重自助聚合，它在不损失精度的情况下将每个数据集的波动率降低40–54%（计算复杂度为K倍ERM）；第二种是我们提出的“双自助法”——两个网络在独立自助采样上联合训练，并通过预测间的对称KL一致性损失进行约束，在匹配2倍ERM计算量的条件下，其波动降低中位数比Bagging-K=2方法额外低45%。跨样本预测波动应在科学机器学习基准报告中与预测性能并列呈现，因为若无此指标，参数侧与数据侧方法在它们实际存在差异的度量上将无法区分。