Out-of-distribution generalization requires exact representational equivalence to the generating mechanism, not statistical approximation—a criterion that constrains inference rather than training and explains why neural networks fail on novel entities while logic-based systems succeed.
This paper argues that models generalize beyond their training data only when they compute representations structurally equivalent to the underlying mechanism—not approximations.