Researchers have developed a method to generate synthetic tabular data that adheres to domain-specific constraints. The approach involves discovering and enforcing three types of inter-column constraints: equations, linear inequalities, and logical dependencies. A unified workflow represents these constraints as machine-executable hypotheses and applies a common interface for validation, diagnosis, and revision. The method is evaluated through behavioral audits and end-to-end evaluations, showing improved performance over direct prompting and preserving univariate marginals. --- Why it matters: This matters to AI researchers because it addresses the challenge of generating synthetic data that is both statistically valid and semantically meaningful. By enforcing domain-specific constraints, this approach can improve the reliability and utility of generated data for downstream applications. Source: https://arxiv.org/abs/2608.15109