Y-RET: A SEMANTIC INVERTED INDEX FOR QUERY-BOUNDED CONTEXTUAL RETRIEVAL

Neural retrievers typically persist learned corpus state at the granularity of every searchable unit, from one vector per sentence in dense retrieval to many contextual vectors in late-interaction systems. We ask whether contextual state must be stored at the same granularity at which relevance is evaluated. We introduce Y-RET, a semantic inverted index that instead persists reusable lexical-semantic representatives and sparse sentence postings, then materializes full contextual sentence states only for a bounded candidate set. Across six retrieval benchmarks and four embedding backbones, Y-RET matches the corresponding dense retriever in macro effectiveness while reducing persistent retrieval state by 71.7–87.8%. On direct sentence-evidence retrieval, where BM25, dense, and Y-RET candidates are reranked by the same contextual verifier, Y-RET matches dense acquisition on SciFact. At 10.1 million sentence units on Natural Questions, the storage reduction grows to 88.4%, with a measurable effectiveness cost under the current scoring configuration. These results show that reusable semantic state can replace a large fraction of corpus-wide neural persistence while full contextual discrimination is deferred to query time.