GRAFT: A Lightweight Shared Generative Backbone for Efficient Cross-Schema Tabular Synthesis

Tabular data underpins decision-making in finance, healthcare, and business intelligence, yet many real-world tables are small, imbalanced, or access-restricted. Predictive foundation models for tabular data have matured rapidly, and a first generation of generative tabular foundation models has emerged. These generators, however, are built on diffusion, transformer, or language-model backbones that are expensive to adapt to a new schema and slow to sample. We ask whether a far lighter design can play the same role where synthetic data matters most. We propose GRAFT, a cross-schema generative model that decouples universal tabular structure from schema-specific detail. A single sub-million-parameter generator, conditioned on a fixed-length meta-feature vector describing the target table, is shared across all schemas. Lightweight per-schema mapping layers project its output into each feature space, and per-schema local discriminators supply specialized feedback without gradient interference. After pretraining on a corpus of heterogeneous public tables, the generator is frozen and adapts to an unseen schema by training only a new mapping layer and discriminator for ten epochs. Under a strict held-out protocol over 86 unseen schemas and three downstream evaluators, GRAFT improves augmented-classifier accuracy over from-scratch CTGAN and TVAE trained for the same or thirty times larger budgets (Wilcoxon p<10−3 in all cases). It also matches the augmentation utility of TabPFGen, a training-free generator built on a pretrained transformer an order of magnitude larger, while producing rows in a single forward pass rather than an iterative Langevin loop. The advantage is concentrated where it matters most: on the smallest fifth of tables GRAFT leads every baseline, including TabPFGen. We also quantify the price of this design, an augmentation-versus-fidelity trade-off.