Relevance-Guided Residual Calibration for Spectral Compression of Dense Retrieval Embeddings

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Spectral compression reduces the storage and scoring costs of dense retrieval by projecting embeddings onto a smaller principal subspace. However, its corpus-derived weighting rules tie all retained directions to a shared spectral trend, without directly accounting for their individual contributions to relevance. To address this, we propose Relevance-Guided Residual Calibration (RRC), a closed-form method for refining spectral weights using relevance judgments. RRC estimates margin-based weights from the means and variances of positive--negative score contributions along each retained direction. It retains directions with positive estimated weights, decomposes their log weights into a fitted power-law trend and a residual, and adds the residual to the compressor's log weights on this support. This allows relevance judgments to adjust individual directions while preserving the compressor's overall spectral trend. RRC applies to PCA, Whitening, and SpecTemp, and incorporates the corrections into the existing transformation shared by documents and queries, without iterative model training or an additional inference stage. Experiments with three encoders on nine datasets covering conventional and reasoning-intensive retrieval show that SpecTemp + RRC achieves the highest mean effectiveness among tested compressors on the BEIR group and BRIGHT at every target dimension. On the five evaluated BRIGHT datasets, it improves over SpecTemp by an average of 2.34 nDCG@10 points at 768 dimensions. The gains also persist under scalar and product quantization, improving the trade-off between per-document storage and retrieval effectiveness. Our code is available at: https://anonymous.4open.science/r/rrc-spectral-compression-B5CE/