提出新方法诊断表格生成模型的列间依赖缺失，定位问题根源。

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.