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S. Culatana

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#artificial intelligence Preprint Sep 2026

Beyond Private Training: The New Landscape of AI Privacy

Retrieval-augmented systems increasingly rely on vector indexes that may retain deleted items in their search graph. Existing deletion interfaces can prevent deleted identifiers from appearing in returned results while still computing distances to their embeddings during graph traversal. We formalize this distinction a...

S. Culatana, Kang Li · 0 citations
#artificial intelligence Preprint Oct 2026

MRVQ: One Resident Index for Dimension- and Rate-Elastic Vector Search

Dense-retrieval services must switch among embedding-prefix dimensions and index bit rates as latency, quality, and memory budgets change. Tuning a quantizer separately for each rate gives the best quality, but the retrieval tier then holds several code streams and quantizer states at once. We introduce Matryoshka Resi...

S. Culatana, Shan Huang, Kang Li · 0 citations

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