Evidence Inheritance in Model Updates: Performance Continuity Is Not Enough

Deployment teams commonly release updated models after data refreshes, continual training, or maintenance, while continuing to cite behavioral evidence from an earlier version. We study evidence inheritance: whether diagnostic evidence established for a reference model M0 remains empirically representative of an updated model Mt. Identification uses a three-level control chain. Level 1 is a magnitude sanity check: positive-scale reparameterizations match the weight-space or representation-space displacement of a claim-breaking update while the prediction function stays fixed, so near-zero diagnostic transfer loss (DTL) is the arithmetic consequence of that symmetry and only closes the false positive that any movement of weights or features would invalidate the diagnostic. Level 2 is the identification. Reliance migrates onto a third channel the inherited diagnostic never probes: the function changes and weight-space displacement exceeds the claim-breaking update, yet the inherited claim remains intact. Level 3 transfers that sanity check, and the claim-breaking phenomenon, to stacked MNIST-CIFAR inputs on a convolutional network. Prediction churn and claim-aligned functional drift (FD) reverse rank between a saturated synthetic task and stacked image inputs. Thus performance continuity is insufficient for automatically inheriting historical diagnostic evidence: continuity must be assessed relative to the historical claim being transferred.