LEMUR and Mean Centering for Late-Interaction Retrieval in txtai

LEMUR, or Learned Multi-Vector Retrieval, extends txtai by converting late-interaction token-vector representations into compact fixed-dimensional vectors that can be searched with conventional dense-vector indexes while approximating MaxSim scoring. Its corpus-specific artifacts are trained from token embeddings, with experiments showing that query-encoder token distributions and validation-based model selection substantially improved quality over training from document-token vectors; trained MLP features are the quality-focused option, while random ELM features provide a lower-cost fallback. In exact-search tests using ColBERTv2 on three BEIR datasets, a 2,048-dimensional LEMUR encoding outperformed both an equally sized and the default 10,240-dimensional MUVERA encoding while requiring one-fifth of the index storage, although the results were limited to one model, machine, and incomplete dataset set. The work also adds configurable mean centering to address highly anisotropic token vectors observed with LateOn models, where batch centering generally improved retrieval by exposing more useful directional variation, but could hurt other models such as ColBERTv2. txtai therefore defaults to batch centering only for models with more than one linear layer, allows users to override or select batch, document, or collection scopes, and requires LEMUR training and indexing to use consistent vector settings.

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