From Temporal Representation to Point Prediction: Auditing Sensitivity to Event–Time Reassignment Across Prediction Stages

Temporal point process (TPP) models are evaluated through predictive distributions, decoded point forecasts, and information accessible from hidden representations, but sensitivity at these stages need not agree. We introduce a same-target audit that reassigns historical inter-event times while preserving event identities and order, the interval multiset, total duration, and the future target. The audit separates raw-history predictability, representation accessibility, native distributional sensitivity, prediction movement, and decoded point-error response. Across five TPP architectures on three cascade datasets, SAHP and AttNHP show strong representation-access and positive native-likelihood responses, yet their operational point-MAE effects are weak or negative. Matched fixed-rate re-decoding shows that the endpoint depends strongly on the selected point functional: on Retweet, conditional-median and conditional-mean readouts restore positive responses, whereas on Cyberbullying and YouTube the conditional median remains negative while the conditional mean is positive. In contrast, S2P2 retains positive point-error responses under both its operational conditional-mean readout and a conditional-median control across all three datasets. These results show that temporal sensitivity can persist in learned representations and predictive distributions while being attenuated, reversed, or preserved at the final point readout. The effect of the point functional is itself model- and dataset-dependent, so temporal reliance should be audited across prediction stages rather than inferred from any single representation, likelihood, or point-error measure.