Aligned Tree-State Transfer for One-Message Cross-Silo Adaptation

Cross-silo personalization often has to work with one model transfer and few receiver labels. We ask when the internal state a shipped tree ensemble computes for a record adds information beyond the score it already reports. SHIFT (SHipped-model Internal-state Feature Transfer) has the receiver run the immutable teacher on its own records, compute a TreeSHAP vector for each, and train a student on that aligned state with the teacher score and its permitted raw features; nothing is uploaded and the source never participates again. Under a restricted interface, where the student reads a subset of features plus the teacher's declared outputs, aligned state improved AUC over an otherwise identical score-only student by $+.0058$ on 18 of 22 tasks after correction, and the gain survived controls that break the pairing between a record and its own state or match that state's width with a random projection. The complete workflow is competitive with 21 published federated and one-shot references, but so is a score-only student built from the same transfer, so that comparison speaks for the one-message transfer rather than the state channel. We also report where the interface stops paying: a label-free leaf reduction of the same state is not separated from it by our tests, extraction costs 20 to 42 times more per record than that reduction, the recalibrated teacher with no student still leads every arm on macro-AUPRC and tuned macro-F1, and adding attribution candidates beyond leaf state leaves a receiver's selected NLL unresolved. The contribution is the interface and its empirical characterization under restricted feature access, not a unique advantage for Shapley attribution.