Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval
Quick summary
arXiv:2608.16918v1 Announce Type: cross Abstract: Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic matching, but single-vector representations may compress multiple technical components, functions, and constraints into a single embedding. We propose Sparse Coverage, an unsupervised semantic retrieval framework that maps local span embeddings to a sparse vocabulary of embedding-space centers. The centers are selected with a coverage-oriented k-center objective, and spans activate nearby centers to
Key takeaways
- arXiv:2608.16918v1 Announce Type: cross Abstract: Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents.
- Dense retrieval improves semantic matching, but single-vector representations may compress multiple technical components, functions, and constraints into a single embedding.
- We propose Sparse Coverage, an unsupervised semantic retrieval framework that maps local span embeddings to a sparse vocabulary of embedding-space centers.
Why it matters
“Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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