A new paper 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 specific constraints. This distinction forces researchers to apply different complexity bounds. Practitioners must now use distinct mathematical frameworks for testing versus verification.