colbert

Late interaction gets cross-encoder-like quality with near-bi-encoder scalability. The signal is the per-token embeddings plus MaxSim matching that buys you the middle ground, and knowing what it costs. Here is the answer.

Late interaction stores one vector per token, so a corpus that fit in a few GB as single vectors can balloon 100x. The signal is knowing the compression and indexing tricks (centroids, residuals, PLAID) that make multi-vector retrieval shippable.