A new Apple paper proves that location-invariant properties of functions diverge from distribution properties during verification. While these two concepts align during testing, the researchers found this relationship breaks down in verification contexts. This distinction forces a rethink of how machine learning models are formally verified. It limits the portability of distribution-based proofs.