Evidence Is Not Exchangeable: Query-Conditioned Evidence Routing for Tabular Foundation Models

Tabular foundation models predict from labeled examples placed in their context. In long-tailed classification, an additional labeled example has no fixed value: the same evidence can repair one query and interfere with another. Evidence selection is therefore a query-conditioned decision, yet existing selection rules often assign candidates a shared value or retrieve them by representation similarity. We formalize evidence value as a query-candidate utility U(q,e), whose query-specific rankings create regret for global selection. QCUR estimates this utility for a frozen backbone from the query representation and the candidate-induced posterior response, then routes one additional example to each query. Across three tabular foundation-model backbones, nine datasets, and three random seeds, QCUR yields a 4.5% relative improvement in mean Accuracy over backbone-only inference, together with a 4.3% relative improvement in Macro-F1 and a 1.1% relative improvement in Macro ROC-AUC. QCUR also exceeds the strongest protocol-matched context-selection baseline by a 3.1% relative improvement in mean Accuracy under the same one-evidence budget. Query-specific ranking inversions, measurable global-selection regret, and repair-dominant prediction changes show that evidence utility varies across queries. Query-conditioned evidence valuation provides a portable principle for improving frozen tabular foundation models.