Head collapse, features stay: why replay needs big buffers

A persistent paradox in Continual Learning is that neural networks often retain linearly separable representations of past tasks even when their output predictions fail. We formalize this distinction as the gap between deep (feature-space) and shallow (classifier-level) forgetting. We demonstrate that experience replay affects these two levels asymmetrically: while even minimal buffers anchor feature geometry and prevent deep forgetting, mitigating shallow forgetting requires substantially larger buffers. To explain this, we extend the Neural Collapse framework to sequential training. We theoretically model deep forgetting as a geometric drift toward out-of-distribution subspaces, proving that replay guarantees asymptotic separability. In contrast, we show that shallow forgetting stems from an under-determined classifier optimization: the strong collapse of buffer data leads to rank-deficient covariances and inflated means, blinding the classifier to the true population boundaries. Our work unifies continual learning with OOD detection and challenges the reliance on large buffers, suggesting that explicitly correcting the statistical artifacts of Neural Collapse could unlock robust performance with minimal replay.