Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data

arXiv:2607.15606v1 Announce Type: cross Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing, yet a generator can reproduce every marginal and every foreign-key relationship while emitting timestamps that run backwards or repeat, and while sending entities along paths that no real entity followed. Conventional tabular evaluation, which pools records into static distributions, is blind to such failures. We present a taxonomy-guided evaluation protocol for temporal fidelity, in which the applicable measurements are determined by the data rather th