Maximum Inner Product is Query-Scaled Nearest Neighbor — HN submission

Mon, 12 Jan 2026 11:20:16 GMT

Show HN: Two-line change, 30% RAG boost

While graph-based vector retrieval algorithms like HNSW and NSG are highly favored for their excellent retrieval speed, it is often overlooked that they are designed for Euclidean space vector retrieval. "Metric mismatch" can be devastating in many scenarios—vector data better suited for maximum inner product retrieval, when paired with Euclidean vector algorithms, often leads to "retrieval results semantically irrelevant to the query." Looking back at the field of maximum inner product retrieval, there has yet to be a groundbreaking algorithm comparable to HNSW or NSG. Many previous works…

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Maximum Inner Product is Query-Scaled Nearest Neighbor — HN submission