Statistical Rank Transport for Efficient Conformal Recalibration After Model Updates

Model updates invalidate cached conformal scores, while full recalibration reevaluates every calibration example with the updated model. We study arbitrary fixed updates through statistical rank transport. A small sample fits a monotone map from cached reference scores to updated scores; an independent residual sample supplies a maximum-residual radius; and a contamination-aware order-statistic argument localizes the updated threshold to a contiguous block that is rescored exactly. The maximum residual induces a distribution-free beta-binomial upper reference for the number of calibration violations, including discrete residual laws, so the contamination allowance is chosen directly from $(r_E,{\delta }_V)$ rather than through an intermediate tolerance parameter. The same target-$k$ construction also yields a training-conditional guarantee after adding the calibration-order margin required by ${\delta }_Q$. On Llama-3-8B backbone LoRA and ViT-B/16 quantization and pruning, the marginal-efficient setting uses roughly 6-18% of full-calibration forwards while stale thresholds materially undercover. Against a matched-budget PAC mini split, the PAC-tuned transport setting reduces the delivered target by roughly 0.3-0.6 percentage points and mean set growth by about 3 percentage points in the evaluated moderate-cache regime. The advantage is not monotone in cache size: at fixed $r_E$ it peaks before boundary-block cost overtakes the statistical benefit, while scaling $r_E$ with the cache preserves the favorable regime over a wider range of $n$.