Construct a neural acceptance or abstention set from calibration samples together with an explicit boundary map selecting the samples that determine the set. If the map is proper projective and its cross-sample complexity profile is stable, the conditional violation risk has an exact beta law indexed by boundary size rather than network parameter count. This provides a falsifiable, distribution-free certificate for neural selective classifiers and learned safety filters.
Convert an LM's probabilities over a controlled set of verbal continuations into probabilities over application states using a fixed semantic map, then calibrate the resulting state vector on held-out labeled examples. This replaces unconstrained verbal confidence with an auditable posterior estimate whose error can be directly evaluated.
Replace raw Pearson correlation when evaluating a temporal neural predictor with a score measuring how many null standard deviations its Fisher-transformed correlation exceeds. Estimate the null scale from a small set of time-misaligned predictions, then reuse it across context lengths or checkpoints. This prevents models from being rewarded for predicting statistically easy, low-information features and gives a more comparable validation signal across datasets and targets.