科学机器学习领域长期忽视预测一致性问题，这篇论文给出了可落地的解决方案。做化学/材料AI建模的团队，建议在基准测试中加上这个指标，否则参数侧和数据侧方法在关键差异上无法区分。
科学机器学习通常只报告预测性能，但忽略了不同训练数据下预测结果的一致性。在9个化学基准测试中，两个独立训练的分类器在总体准确率上差异仅1.3-4.2个百分点，但对8.0-21.8%的测试分子预测标签不同，这种差异被称为跨样本预测波动。标准参数侧方法（如深度集成、MC Dropout、随机权重平均）无法减少这一差距，而两种数据侧方法有效：K-自助法装袋可将波动降低40-54%且不损失准确率，以及作者提出的双自助法（twin-bootstrap），在相同计算量下进一步减少45%的波动。该研究建议在科学机器学习基准报告中增加跨样本预测波动指标。
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 $1.3\text{--}4.2$ 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.