{"ID":3084688,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-06T20:54:36.964885582Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05441","arxiv_id":"2606.05441","title":"GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data","abstract":"We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones. We introduce Graph-guided Ordering with Local Refinement (GO-LR), show its equivalence to weighted Minimum Linear Arrangement, and interpret the practical solver as a TSP-path-style surrogate. We propose GOTabPFN,which builds on GO-LR, and a Neuro-Inspired Subunit Compression (NSC) unit to pool locally adjacent ordered features into meta-features, yielding a compact representation that makes TabPFN-style prediction practical in HDLSS regimes. Across tabular benchmarks, GOTabPFN improves stability and accuracy under tight token budgets.","short_abstract":"We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones. We introduce Graph-guided Ordering with Local Refinement (GO-LR), show its equivalence to weighted Minimum Linear Arrangement, and interpret the pract...","url_abs":"https://arxiv.org/abs/2606.05441","url_pdf":"https://arxiv.org/pdf/2606.05441v1","authors":"[\"Al Zadid Sultan Bin Habib\",\"Md Younus Ahamed\",\"Prashnna Kumar Gyawali\",\"Gianfranco Doretto\",\"Donald A. Adjeroh\"]","published":"2026-06-03T21:03:33Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ML\"]","methods":"[]","has_code":false}