On Reranking Space for Multi-Tenant Retrieval with Adapted Queries

Abstract

Multi-tenant dense retrieval systems increasingly employ shared compressed indexes where individual tenants adapt embeddings via fine-tuning (e.g., LoRA). While query-side projection adapters bridge the resulting embedding mismatch, a critical design choice remains for the optional reranking stage: should distances be computed in the original index space (source-space) or the adapted query space (target-space)? Contrary to the intuition that the calibrated target-space should perform better, we find the opposite to be true. Across 23 LoRA-adapted dataset-tenant pairs, four adapter architectures, and four index configurations (ranging from PQ-16 to HNSW), source-space reranking consistently outperforms target-space reranking, improving nDCG@10 by more than 10 percentage points in some cases. We further evaluate distance blending between these signals, finding that it provides robust gains on coarse indexes (e.g., PQ-16) when the reverse adapter is structurally sound, while adding minimal latency. Our results offer a straightforward heuristic for multi-tenant platforms: maintain the shared index, project queries using existing adapters, rerank in the source-space, and apply distance blending as latency permits when working with low-precision indexes.