AI RESEARCH
Efficient Vector Search in the Wild: One Model for Multi-K Queries
arXiv CS.LG
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ArXi:2603.06159v1 Announce Type: cross Learned top-K search is a promising approach for serving vector queries with both high accuracy and performance. However, current models trained for a specific K value fail to generalize to real-world multi-K queries: they suffer from accuracy degradation (for larger Ks) and performance loss (for smaller Ks)