PAIRCAUSE: Exact-Marginal Diagnostics of Multimodal Readout and Acceptance

When multimodal systems accept more errors under a common perturbation, is information lost, read out incorrectly, or simply admitted by the acceptance rule? PAIRCAUSE diagnoses these alternatives by changing the pairing of complete modality trajectories while exactly preserving their label-conditional empirical multisets. On 80 synthetic test blocks, shared pairing raises ERM accepted error from 23.32% to 46.63%, predominantly through joint-state composition; simple linear and strong-cue controls reproduce the direction. Distribution-aligned probes reveal a different phenomenon: on the same shared-mixture assessment blocks, refitting a linear head on frozen ERM features raises accuracy from 50.96% to 87.53%, but raw-input and random-feature controls perform similarly. Conversely, a clean-fitted linear head reduces Coupled's shared accuracy from 88.04% to 52.81%. Separating classification, fixed-mask risk, and recomputed acceptance shows that cue restoration combines genuine correction with rejection. Twenty retained fits and 36 new same-backend fits support these distinctions. The contribution is an auditable feature-space diagnostic of recoverability, readout supervision, and acceptance, not evidence of a uniquely learned causal representation or a demonstrated physical sensor attack.