A new study from Apple Machine Learning Research proves that location-invariant properties of functions diverge from distribution properties during verification. While these two concepts align during testing, they separate when verifying results. This distinction forces researchers to rethink how they validate symmetric functions. The findings refine the theoretical bounds for algorithmic verification.