Honey, I shrunk the embeddings: Matryoshka vs. PCA

Experiment compares Matryoshka Representation Learning — which trains embeddings to pack information into early dimensions — against post-hoc PCA, testing both across eight retrieval-quality datasets. MRL requires models trained with prefix-length losses; PCA can shrink vectors from any embedding model. Code and data are on GitHub.