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About this paper
Y. Du
The authors estimate query-level retrieval performance from dense query embeddings using two signals: stability after quantization and reconstruction, and distances to nearest neighbors in the index. They combine these measures as proxies, not calibrated success probabilities. On MS MARCO Passage Ranking, the method reports Recall@10 of 0.673 versus 0.659 for Deep Ensembles, a 2.1% relative improvement; adaptive retrieval remains untested.
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