CART: Contextual Cell Reliability for Robust Tabular Prediction

Real-world tabular data is rarely uniformly reliable: individual entries (cells) can be corrupted or mis-recorded while the rest of a record remains trustworthy, yet most robust tabular learning methods act at the sample level (train the whole model to be robust), the feature level (weight entire columns), or attempt to reconstruct every cell indiscriminately. We argue that none of these directly answers the question a predictor implicitly has to resolve at inference time: *how much should this particular observed cell be trusted for this particular record?* We propose CART, an architecture that (i) estimates a contextual, per-cell reliability score from full-record attention, (ii) reconstructs a true leave-one-out (LOO) counterfactual representation of every cell using an attention query that never observes the cell itself, and (iii) adaptively fuses the observed and reconstructed representations according to the learned reliability before classification. Across eight tabular benchmarks, three seeds, and six corruption severities, CART attains the best noise-robustness AUC on 7 of 8 datasets and the best or tied-best accuracy at 20% corruption on 6 of 8 datasets, while remaining competitive on clean data. A seven-way mechanistic ablation shows that the improvement is not explained by indiscriminate reconstruction, a fixed repair ratio, a random gate, or even a gate with the correct value distribution but permuted cell assignment (two-sided Wilcoxon signed-rank test across 8 datasets, $p=0.0078$ in all three comparisons): the gain requires assigning the *correct* reliability score to the *correct* cell. Compared with reconstruction-oriented baselines built on the same tokenizer (a denoising autoencoder, a transformer autoencoder without LOO, and a variational autoencoder), CART attains the best cell-level reliability detection on roughly half of the datasets and is a close competitor elsewhere, showing that generic reconstruction quality and cell-reliability detection are related but distinct capabilities. Finally, CART's robustness advantage persists, though it does not always dominate, under corruption magnitudes and feature shifts not seen during training.