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This work introduces EigenLI, a spectral approximation framework that compresses late-interaction representations via document-specific low-dimensional subspaces and consistently outperforms comparable single-vector surrogates such as MUVERA.
This work provides the first explicit family of query and document sets, together with their relevance matrices, for which single-vector embeddings that rank all relevant documents above irrelevant ones require exponential size, whereas polynomial-size multi-vector embeddings suffice.
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