Generative models for tabular data synthesis, CTGAN and the diffusion-based TabDDPM, are high in statistical accuracy but fundamentally agnostic to domain-level semantics, often generating records that are statistically plausible but logically nonsensical. We call this failure mode "semantic blindness." Standard evaluation metrics: Kolmogorov–Smirnov (KS) distance, Train-on-Synthetic-Test-on-Real (TSTR) accuracy, and privacy scores constitute no structural barriers to record-level logical violations, and they are not detectable. In this paper, we present NeuroGuard, a realistic systems-level neuro-symbolic framework that extends any pre-trained tabular generative model with a deterministic post-hoc semantic guardrail without requiring retraining. NeuroGuard, by integrating a deterministic Rule Engine (RE) for unambiguous single-attribute violations and LLM reasoning (open-source LLaMA-3-8B as the main model and GPT-4o as the secondary, high-level reference) for sophisticated multi-attribute violations, cuts LLM API dependency by 68% when compared to a pure LLM method. We present and verify LVR and find that by means of downstream degradation experiments (an AUROC drop of 0.048 at 10% LVR injection), inter-annotator agreement (κ = 0.74), and SDMetrics correlation (r = −0.81) show that LVR is orthogonal to existing metrics describing the failure mode. We also show that LVR is not a bad substitute for SDMetrics, but instead it is something orthogonal that addresses a gap left by the logical dependency score of SDMetrics. Across the four heterogeneous real-world datasets—Adult Census Income, Healthcare Stroke Prediction, German Credit, and large-scale PaySim Financial Transactions (n > 500K)—a 10.1–17.3% relative decrease of the LVR rate (p < 0.01, bootstrap 95% CI) is observed while maintaining TSTR accuracy and KS fidelity. Ablation demonstrates that the LLM layer adds an extra 5.1284% resolution improvement as compared to its RE counterparts for multi-attribute violations. Average F1 = 0.821 for Automated Constraint Discovery. The discussion of limitations, including constraint quality dependence, privacy trade-off, and open-source model comparison, is transparent. In our view, no previous study employs such a combination of post-hoc model-agnostic enforcement, open-source LLM testing, multi-domain LVR validation, and systematic ablation.
Konar et al. (Sat,) studied this question.