Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models
This work characterizes statistical separability in probabilistic AI models by linking challenge-wise prover behavior to verification performance and required evidence. It develops a method where a verifier estimates discrepancies using a second-order U-statistic across multiple challenges to determine the necessary number of challenges (N) and responses per challenge (m) needed to achieve a target detection accuracy.