Better null tests cut certified AI memorization cases sharply
A new audit says generative-model memorization claims can change dramatically once similarity scores are tested against a real null distribution, which matters for anyone measuring data copying or mitigation quality. Using matched controls and false-discovery control, the paper certifies 36 of 61 MemBench images, while held-out controls appear in just 0.01% of calibration splits. Its stricter permutation test finds near-duplicate copying in none of the twenty-four generators examined.