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 result: setting this cap directly to two---making every pretrained classification task binary---still yields weights that perform zero-shot K-way classification 2. On the 38 TabArena classification datasets (200 bootstrap rounds, anchored at default RF = 1000), the binary two-stage recipe reaches 1590.1 Elo, above the official default at 1568.0 (+22.1), above TabPFN-v2.6 (1577.1)...
| Authors | Zhixiong Cheng, Shiyuan Han, Jingjing Suo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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