Learning Query Encoders Can Be Hard Even When Vector Retrieval Is Geometrically Easy
Efficient vector retrieval requires both a corpus geometry that supports retrieving the right documents through vector similarity, and a query encoder that can embed queries near their desired documents in the embedding space. Recent work has studied geometric capacity through the lens of the minimum embedding dimension needed to realize all top-$k$ answer sets of $n$ documents. We study a different notion of geometric capacity--the maximum recall achievable for a frozen document index--and explore whether learned query encoders can reach this ceiling. On several real-world retrieval benchmarks, we show that retrieval quality of single-vector query encoders often lies far below what the d...
| Authors | Anders Wikum, Nina Mishra, Amin Saberi et al. |
|---|---|
| Primary category | cs.IR |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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