GLIE preprint: late-interaction retrieval vectors compress to ~5 degrees of freedom
inductionheads · x · 2026-09-11
New preprint GLIE (Generative Late-Interaction Embeddings) tackles storage bloat in visual document retrieval: ColBERT-style late-interaction retrievers store 1,000 vectors per page, yet those vectors have only 5 degrees of freedom and an exploitable geometry.
The approach:
- Store only a small set of projected vectors as the index
- Train a tiny shared decoder to regenerate the full embedding set from the pooled ones
- At query time, retrieve candidates with the compressed vectors, then decode and rerank with the full embeddings
Author antoinechaffin sees it as bridging the gap between ColBERT and MICE. Directly relevant for developers facing vector storage costs at scale.
Related event: GLIE Paper Compresses Late-Interaction Retrieval Storage 200x(4 posts)→
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