Making tabular AI work when you have more columns than rows

Tabular foundation models like TabPFN struggle when datasets have thousands of features but few samples—a common real-world scenario. GOTabPFN solves this by intelligently ordering features using graph theory, then grouping adjacent ones into meta-features to stay within token limits. The approach improves both stability and accuracy across benchmarks without retraining large models.