Auditing Reference Access in Frozen Feature Anomaly Detection

Frozen-feature anomaly detectors are frequently compared unfairly under mismatched reference-access protocols: inductive methods rely on clean normal training images, while transductive methods leverage the full test set that contains anomalies. We present BudgetMatch, an auditable evaluation contract to formalize reference-access specifications and enforce valid single-factor comparisons. With the scoring function fixed, we conduct pre-registered, controlled experiments across MVTec AD, VisA and BTAD to quantify model sensitivity to the budget of clean reference samples. We find that increasing clean reference samples yields consistent gains in macro image-AUROC, and this magnitude of performance shift is comparable to the reported improvements of many published anomaly detection algorithms. The transductive full-pool setting differs across eight protocol dimensions and should be treated as a full-protocol comparator rather than a single-variant baseline. We release machine-verifiable certificates, demonstrating that leaderboard rankings can only be reliably interpreted when reference-access rules are fully disclosed.