What Do Collapse Diagnostics Certify? Testing Alignment Objectives for Orthographic Shortcuts

Objectives that align representations without negatives are judged by label-free diagnostics of collapse, effective rank and uniformity, which also choose their hyperparameters. We ask what these diagnostics certify about what a representation has learned. In cross-lingual alignment from parallel text a model can match related languages by shared spelling rather than meaning; a substitution cipher on one side of every such pair removes the shared letters with the supervision held byte-identical, and the fraction of a model's score kept under the cipher is how much of its alignment was not spelling. The diagnostics certify spread, not content. (1) At the weights their effective rank selects, the two anti-collapse devices, VICReg and SimSiam, reach contrast's rank or beyond and keep 0.16 and 0.29 of their score against contrast's 0.65. (2) Tuning brings both within reach of contrast while the rank stays flat, and the spectrum every diagnostic reads does not move under the cipher. (3) Selecting by these diagnostics favours a shortcut solution, and on a standard pretrained encoder a run that learned nothing. The objectives that learn the lexicon share a target that cannot drift toward the shortcut, the batch in contrast or a frozen teacher in distillation: attraction to a frozen teacher learns the lexicon as contrast does, without negatives. Retrieval of the training pairs, which needs no labels, tracks held-out accuracy at ρ = 0.96 where rank selects the shortcut. Anti-collapse devices should be selected by a task-shaped criterion and tested with the shortcut removed, not certified by spread alone.