TabWorld: A World-Modeling Foundation Model for Tabular Generation

Tabular foundation models (TFMs) are promising for learning reusable priors for heterogeneous tables in low-data regimes. Yet most TFMs are trained to predict labels or reproduce passively observed rows. In small downstream tables, these observational objectives can rely on noisy empirical correlations rather than infer a table-level structure that supports coherent generation and adaptation. We propose TabWorld, a world-model-inspired generative TFM that learns a table-adaptive simulator: from support rows and a query context, it generates full rows and, when a structural causal model (SCM) action is supplied, predicts structured action responses. TabWorld combines observational diffusion training on real tables with query-conditioned world-model supervision, using real tables for support-conditioned completion and synthetic SCMs for oracle pre/post-action labels unavailable in ordinary observational data. Across unseen-table generation, completion, low-data adaptation, and SCM intervention diagnostics, TabWorld improves post-adaptation fidelity, utility, and SCM intervention-diagnostic behavior over observational-only baselines.