Jing-Jing Suo

Two Is Enough: Binary Classification Priors Suffice for Multi-Class In-Context Tabular Prediction

Tabular foundation models (TFMs) pretrain a transformer on large-scale synthetic priors so that a single forward pass yields zero-shot tabular prediction. TabICLv2 samples the class count of its classification prior during pretraining, and the prevailing configuration caps it at ten. We report a counter-intuitive resul...