Training dynamics encode global structure—persistent long-range correlations, representational curvature, andseasonality clusters—that no individual sequence contains. While standard memory mechanisms extend context withina sequence, they ignore a complementary information source: the training trajectory itself. We propose SpectralMemory, a mechanism that captures hidden-state evolution across thousands of mini-batches to encode temporal structureunavailable in any single sequence.