Decision-TabPFN: Separating Action Scoring from Inventory Control

A frozen predictor can select low-loss actions on fixed queries yet yield a weaker sequential policy. We study this gap in perishable inventory using Decision-TabPFN, which scores immediate profit from completed transitions. Matched comparisons show a TabPFN advantage over XGBoost on Decision-TabPFN histories, but this advantage varies with the source of queried states. Holding the backbone fixed, independently selected forecast-to-order pipelines earn more profit in two of three regimes. Fixed-budget context comparisons show that offline rankings need not persist online. To connect prediction with control, we bound inventory-dependent continuation value independently of the remaining horizon and cancel forecasting error shared by policies. The resulting inequality separates demand-prediction error from model-planning error and yields sublinear dynamic-optimum regret under explicit cyclic-demand learning and complete-horizon planning. A separate block-planning study exposes strong prior sensitivity and no demonstrated benefit from TabPFN proposals. Together, the results distinguish action scores on deployment states, the supplied decision structure, and continuation value as separate evaluation targets.