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Life sciences · Preprint
arXiv · August 14, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint describing a two-stage diffusion transformer framework for generating synthetic tabular data from multiple heterogeneous tables. The authors report high fidelity in statistical representations and favorable fidelity-diversity trade-offs in generated synthetic data, but no clinical validation, benchmarking against established methods, or real-world health data applications are presented in the abstract.
Preprint. Intervention: Two-stage cross-tabular data generation framework using diffusion transformer model.
Two-stage framework transforms heterogeneous raw tables into standardized statistical tables with identical columns across all inputs Diffusion transformer model captures structural patterns across homogeneous statistical tables and generates synthetic statistical tables Synthetic raw tables reconstructed via multivariate Gaussian sampling and inverse probability integral transform
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This is a methods/technical paper describing a novel algorithm for synthetic data generation with experimental validation on statistical properties, but it lacks clinical validation, real-world deployment, or comparison to clinical outcomes.
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Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic tabular data generation methods are largely restricted to single-input-table scenarios and struggle to effectively handle multiple heterogeneous tables with diverse feature sets. To address this limitation, we propose a two-stage framework for cross-tabular data generation. In the first stage, each heterogeneous raw table is transformed into a standardized statistical table with the same set of columns across all tables. Each statistical table captures the marginal distributions of the original columns and the pairwise correlations among them. In the second stage, a diffusion transformer model is trained to capture structural patterns across these homogeneous statistical tables and to generate synthetic statistical tables. Synthetic raw tables are subsequently reconstructed from the generated statistical tables via multivariate Gaussian sampling followed by an inverse probability integral transform. This two-stage CTDG framework enables the learning of a unified generative model from multiple heterogeneous tables and supports the generation of an unlimited number of realistic synthetic heterogeneous tables. Experimental results demonstrate high fidelity in the learned statistical representations and a favorable fidelity-diversity trade-off in the generated synthetic data, validating the effectiveness of the proposed approach.
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