ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs

ArXi:2512.09953v2 Announce Type: replace-cross Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, We On Vision Transformer classification tasks, ZK APEX recovers nearly all personalization accuracy while effectively removing the targeted information. Applied to the OPT125M generative model trained on code data, it recovers around seventy percent of the original accuracy. Proof generation for the ViT case completes in about two hours, than ten million times faster than re.