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arXiv · 2610.31932

MICE to Meet You: From Re-ranking Only to End-to-End Retrieval

Victor Morand·Yuxuan Zong·Mathias Vast·Josiane Mothe·Benjamin Piwowarski
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Abstract

Late-interaction models offer state-of-the-art efficiency-effectiveness trade-offs for neural retrieval by enabling document representations to be pre-computed and efficiently indexed. Yet, their relatively simple interaction mechanisms, typically MaxSim, can limit their effectiveness compared to cross-encoders. MICE, a recently introduced re-ranking architecture, aims to combine cross-encoder-like effectiveness through expressive transformer-based scoring with reduced inference-time computation through document-side pre-computation. However, no practical way to store these document representations was explored. We address this limitation here and introduce MICE-P, the first end-to-end retrieval pipeline with MICE, based on PLAID to index the contextualized document token representations. We conduct a controlled comparison of MICE, ColBERT and cross-encoders by training comparable models using the Ettin Family under matched experimental conditions. Our results show that MICE-P defines a new class of retrieval architecture with an efficiency-effectiveness trade-off of its own: it matches late-interaction retrieval throughput while retaining the effectiveness of cross-encoders.

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Victor Morand, Yuxuan Zong, Mathias Vast, Josiane Mothe, Benjamin Piwowarski. 2026-10-22. MICE to Meet You: From Re-ranking Only to End-to-End Retrieval. https://arxiv.org/abs/2610.31932

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