The Moving Blind Spot in OOD Detection: Mixing Makes Universal Rates Uniform

An out-of-distribution (OOD) detector learns from in-distribution (ID) data and is judged on a distribution it never observes. Problems sharing an ID law are indistinguishable and form an observation fiber, on which every learner is a schedule fixed in advance. The sample size is only a clock, and since a clock can run at any speed, a fiber admits every universal rate or none. We ask which, for errors against a detector class at all ID/OOD proportions. A fiber can be consistent without being uniformly learnable: each hidden law is eventually served, yet at every time some law is missed, a moving blind spot. Mixing hidden laws preserves consistency and the worst-case value of a fiber, yet our main theorem shows it turns universal rates into uniform ones: a learner attains a rate on the countable-mixture completion of a fiber exactly when it attains it uniformly. The key step, that consistency makes the class-relative optimal risk affine under mixing, holds verbatim for AUC and any criterion linear in the hidden law. Once hidden laws can be mixed, a moving blind spot admits no rate: one hidden law, condensing the blind spots of infinitely many times, defeats any candidate rate. Nontrivial rates arise only across fibers: selection costs an exponentially small error over finitely many ID laws, a sub-exponential one over countably many, and can defeat consistency over uncountably many, by Baire's theorem. With unrestricted detectors on measure-separated domains, all slowness is statistical, from an exact $1/n$ rate to none.