Blind by Construction: Record-Level Unlearning Audits Miss Retained-Carrier Reconstruction on Frozen Time-Series Features

A record-level unlearning audit compares a model that unlearned a deleted record with one that never saw it, so it is blind by construction to what the retained records reveal about the deleted subject. When a deletion covers a subject with many records, a correlated record from another subject stays in the pool; we call such a record a retained carrier. For frozen time-series features with a ridge readout, we derive an exact identity for how much a carrier adds to reconstructing the deleted subject. Measuring this contribution requires finding carriers, and cosine similarity on frozen features fails here: the features share one dominant direction, so it marks almost every retained record as a carrier and inflates the contribution. After de-centering, the artifact disappears: the all-subject median excess is 0.000 on CWRU, PTB-XL and MIMII and +0.046 on Paderborn. Planted carriers with known effects often go undetected, so these zeros state the measurement's detection limit rather than the absence of leakage. Membership inference on the same data remains accurate. The two channels measure different quantities: whether a deleted record is still distinguishable, and how much a retained carrier lets an attacker reconstruct the deleted subject. Unlearning evaluations should report both, each with its measurement range.