Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
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MV-HNSW is the first native hierarchical graph index for multi-vector data, achieving over 90% recall with up to 14x lower search latency than prior filter-and-refine approaches across seven datasets.
Interviews show data leakage knowledge in automotive perception is widespread yet fragmented by role, with prevention relying on experience and sharing rather than specific tools, framing it as a socio-technical coordination issue.