LEARNING WHAT MATTERS: REWEIGHTING FROZEN EMBEDDINGS FOR DENSE RETRIEVAL

Dense retrievers enable efficient semantic search using query and document embeddings. Once deployed, however, improving ranking quality of a dense retriever requires careful balancing of its performance with the cost of changing existing retrieval infrastructure. This motivates adapting how retrieved documents are scored while retaining the encoder, stored embeddings, and index. Yet fixed similarity scoring does not adapt dimension weights to the candidates competing for relevance. We introduce Shared Signed Gate (SSG), a lightweight reranker that learns which embedding dimensions help distinguish relevant documents from retrieved hard negatives. Using explicit dimension-level supervision, a small network predicts candidate-conditioned residual weights from the query and candidate statistics. Across nine encoders and seven BEIR datasets, SSG improves average nDCG@10 for every encoder and achieves the highest average among evaluated baselines for all three encoders used in detailed comparisons. It also improves rankings produced by other retrieval methods. With BGE-S, SSG adds approximately 7.8% to measured end-to-end latency while preserving the encoder, corpus embeddings, and retrieval index. These results show that candidate-conditioned dimensional weighting can improve ranking quality while reusing the representations and retrieval infrastructure already in place.