Tabular foundation models face a feature-side scaling dilemma: full-width pairwise mixing grows quadratically with the number of columns, whereas feature selection saves memory by discarding evidence.
We introduce Support-Compiled Feature Folding (SCFF), a training-free inference framework that resolves this dilemma without changing the frozen backbone.
SCFF routes support-ranked features through bounded leaves of the native feature encoder, support-checks the residual evidence, and merges the encoded messages before a single contextual prediction.
It thereby converts quadratic feature-interaction work into linear-in-width work with a bounded local working set, without ensembling predictions or training new parameters.
On the exhaustive 18-dataset wide-table slice of fixed AMLB-29, TabZilla, and TabArena snapshots, SCFF improves dataset-macro accuracy and NLL on all six evaluated backbones.
All four matched-width comparisons retain favorable 95 percent dataset-bootstrap intervals on locked folds, with relative error reductions up to 26.1 percent.
Median paired GPU-memory savings are 2.09x to 2.36x, and the ratio of separately observed maximum peaks reaches 34.3x.
Under a measured peak-memory ceiling, SCFF uses the saved budget to preserve more support-selected evidence, improving accuracy by 4.06 and 3.72 points over the widest feasible single leaf on predeclared wide-Core strata of TabICLv2 and TabPFN-3.