发表机构
Accenture Japan（埃森哲日本公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究表格生成模型列间保真度，引入依赖感知保真度诊断方法，分解XGB-C2ST测试，应用于TabbyFlow/EF-VFM发现标准指标遗漏的依赖差距，探讨差距原因，表明是缺乏直接依赖监督，非容量或结构问题
AI 中文摘要
合成表格数据不仅因其保留了每列的边际分布而有价值，还在于其保留了列之间的依赖性，这种结构在欺诈和临床风险等不平衡领域中承载了许多少数类别的判别信号。然而，研究表明，最常用于验证合成表格数据的指标在很大程度上对列间依赖性视而不见。我们引入了一种依赖感知保真度诊断方法，将一个强大的分类器两样本测试（XGB-C2ST）分解为边际、依赖和数值-分类交叉组件，以最坏情况的完全分解参考（所有依赖性被破坏）和最佳情况的真实数据预言机为锚点。将其应用于一个先进的流匹配生成器（TabbyFlow/EF-VFM）时，我们发现了标准指标遗漏的实际依赖差距；完全破坏依赖性会使少数类效用崩溃，并且生成器的残余差距带来较小且一致的效用成本。接着我们探讨这种差距是否反映了平均场生成目标的结构限制，结果表明并非如此，差距顽固存在，即使模型容量增加16倍也无法消除，这表明缺乏直接的依赖监督而非容量或结构限制。
英文摘要
Synthetic tabular data are valued for preserving inter-column dependency, yet each routine fidelity score is a single number that says neither where that dependency is lost nor why. We localize the deficit inside a single score. Equipping a classifier two-sample test (C2ST) with a gradient-boosted discriminator, we decompose it by controlled permutation into marginal, dependency, and numerical-categorical cross components, each read against a fully factorized reference that destroys all dependency while leaving every marginal intact, and against a real-data oracle. The linear detection score in common use rates that same reference as nearly real, a known weakness we replicate on four benchmarks. Applied to a flow-matching (TabbyFlow) and a diffusion (TabDiff) generator, the decomposition exposes a dependency deficit of the same order in both, and we then narrow its source by elimination: it is not a structural limit of the mean-field objective, it is not sampling discretization, and a 16x capacity increase does not close it, while the same measurement responds sharply when capacity is instead cut eightfold, so the plateau is measured rather than a blind spot. Destroying dependency outright collapses minority-class F1 by 0.38-0.61, which bounds what is at stake, though the generators' much smaller residual deficits do not predict their remaining shortfalls. What survives points at the objective: nothing in it scores the joint. Cheap remedies are no substitute: an explicit cross-coupling module and a post-hoc copula both leave the deficit in place.
