Metamorphic testing of learned AC optimal power flow surrogates reveals representation dependence

Changing bus order or an angle reference leaves the AC optimal power flow (AC-OPF) problem unchanged, but can change a learned surrogate's prediction. We test this dependence with thirteen metamorphic relations, each specifying when a transformation is valid and how to compare its outputs. Our screen covers 17 configurations: two released trained checkpoints and 15 random-weight architecture or mechanism controls. Every configuration fails at least six relations, including at tolerances ten times tighter or looser. For \texttt{GridSFM-Open v1.1}, source inspection and intermediate-tensor tests point to an indexing bug and three preprocessing choices; a repaired checkpoint remains untested. In a separate case500 experiment, augmentation reduces transformed-input error in three of four models and median discrepancy and constraint-violation P95 in all four. Most of the large Laplacian-model accuracy gain comes from a GPU/CPU eigensolver mismatch. Some relations worsen, and every evaluated prediction remains AC infeasible. Evaluating multiple encodings exposes failures that accuracy and feasibility scores on one encoding can miss. These results show why evaluations of learned physical surrogates should test consistency across equivalent representations alongside accuracy, optimality, and feasibility. For learned power-grid models, including foundation-model checkpoints, these are the criteria by which foundation-model claims should be evaluated under realistic conditions.