UIBK-Embedding-v1: A Dense and a Late-Interaction Retriever in One Model, One Index and One Search

UIBK-Embedding-v1 is an Apache 2.0-licensed English retrieval model with 179 million parameters that combines dense-vector and ColBERT-style late-interaction retrieval within one encoder, one PLAID index, and a standard MaxSim search rather than requiring separate models, indexes, and score fusion. Built on ModernBERT-base, it uses shared layers with separate dense and token-focused heads, encodes the dense representation into three indexed vector slots, and uses an added tag dimension to prevent dense slots and token vectors from mixing during matching, producing a weighted sum of both retrieval signals. The model was trained on public data through contrastive pretraining, head-specific fine-tuning, hard-negative mining, and cross-encoder distillation directed at the final fused score. Reported results include 59.79 nDCG@10 on BEIR, 70.55 on NanoBEIR, and 61.55 on decontaminated BEIR, exceeding similarly sized dense and late-interaction models and, on some benchmarks, larger alternatives, while using an index comparable in size to a late-interaction model and less storage and encoding time than a separate dense-plus-ColBERT hybrid. It is intended for use through PyLate with standard PLAID infrastructure, but is limited to English, requires remote model code and specified prompts, retains the larger token-level index footprint of late-interaction retrieval, and has not been tested with indexes that transform vectors differently. Community discussion also notes that its results demonstrate replacement of the dense and late-interaction components of a hybrid system rather than lexical retrieval, as BM25 still performs better on the LIMIT benchmark, and that performance on very long queries where critical terms occur beyond the token head’s first 64 tokens remains to be examined.

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