DN-Flow: Driver–Navigator Structured Flow Matching for Mixed-Type Tabular Data Generation

Generating high-fidelity mixed-type tabular data remains challenging because such data contain both numerical and categorical features; different columns often exhibit substantial heterogeneity in marginal distributions, sparsity patterns, and dependency structures. Although recent diffusion-based and flow-based methods have improved generation quality, most methods still rely on a unified generation framework that models all dimensions in a homogeneous manner, without explicitly accounting for the fact that different columns may differ in their suitability for strong local refinement. To address this issue, we propose a driver-navigator structured flow-matching framework (DN-Flow) for mixed-type tabular data generation. Inspired by the collaboration between the driver and navigator in rally car racing, DN-Flow explicitly decomposes the velocity field into a Driver branch (for globally stable transport) and a Navigator branch (for endpoint-aware local refinement). On top of this structured decomposition, we introduce a learnable column-wise soft selector and a dynamic correction control mechanism to jointly model column-level suitability for local refinement and the actual strength of correction injection in an end-to-end manner. Experiments on eight real-world datasets show that DN-Flow consistently outperforms strong baselines in data fidelity and downstream utility, achieves the best Shape performance on all eight datasets, and remains highly competitive on Trend and machine learning efficiency. These results suggest that structured and selective velocity correction offers an effective new paradigm for mixed-type tabular data generation. The code will be released upon acceptance.