Embedding Surgery: query-time localized vector edits fix dense retrieval rankings, +60% nDCG@10
_reachsumit · x · 2026-09-07
A new arXiv paper introduces Embedding Surgery, a lightweight method for adaptive ranking correction in dense retrieval: instead of retraining or rebuilding static indexes, it applies small, localized updates to selected document embeddings at query time, guided by editorial labels, user interactions, or LLM pseudo-labels.
- Formulated as a convex optimization problem that enforces ranking constraints while minimally modifying affected document representations
- Preserves the global structure of the embedding space and runs at low computational cost
- Integrated into standard dense retrieval pipelines and evaluated on TREC Deep Learning, TREC Robust, TREC CAsT, and MS MARCO
- Consistent gains even under noisy or shifting feedback — up to +60.64% relative nDCG@10 on DL-Hard with editorial feedback
A practical path for search, recommendation, and RAG systems to absorb feedback without retraining.
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