{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FVL2SRTPHQY6PHXQ2RH4ZTJJTK","short_pith_number":"pith:FVL2SRTP","schema_version":"1.0","canonical_sha256":"2d57a9466f3c31e79ef0d44fcccd299a8ce5c176023adaf2ac16c3b9e3b77642","source":{"kind":"arxiv","id":"2606.03225","version":1},"attestation_state":"computed","paper":{"title":"HRNN: A Hybrid Graph Index for Approximate Reverse k-Nearest Neighbor Search on High-Dimensional Vectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.DB","authors_text":"Mingyu Yang, Wei Wang, Wentao Li, Wenxuan Xia","submitted_at":"2026-06-02T06:35:54Z","abstract_excerpt":"Reverse k-nearest neighbor (RkNN) search returns all data points that regard a query vector as one of their k-nearest neighbors (kNNs). Existing RkNN methods typically follow a filter-and-verification framework: vectors near the query vector are first collected as candidates and then verified against their kNN-radius (i.e., the distance to their k-th nearest neighbor). However, existing methods face two key limitations in high-dimensional spaces. First, nearby vectors often do not belong to the query's true RkNN set, resulting in excessive candidate expansion overhead. Second, existing methods"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.03225","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-06-02T06:35:54Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"93a26e56320b41e7d8eee56f37a8ead1911a6eef5a9a9626068498d9618a0a0a","abstract_canon_sha256":"a8c8a4f8cdfee3df7174d750f173941e50954d4112b2b30b61ca45741bc753bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:35.363991Z","signature_b64":"3Nd+j6hAFn3S1zhQX2M6rox2tRtqYD8FbdikpecQVFv9rOvrVWQvocQw4xYOoRFt3bJ/wHsZmc6JSaJMRByZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d57a9466f3c31e79ef0d44fcccd299a8ce5c176023adaf2ac16c3b9e3b77642","last_reissued_at":"2026-06-03T01:05:35.363587Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:35.363587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HRNN: A Hybrid Graph Index for Approximate Reverse k-Nearest Neighbor Search on High-Dimensional Vectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.DB","authors_text":"Mingyu Yang, Wei Wang, Wentao Li, Wenxuan Xia","submitted_at":"2026-06-02T06:35:54Z","abstract_excerpt":"Reverse k-nearest neighbor (RkNN) search returns all data points that regard a query vector as one of their k-nearest neighbors (kNNs). Existing RkNN methods typically follow a filter-and-verification framework: vectors near the query vector are first collected as candidates and then verified against their kNN-radius (i.e., the distance to their k-th nearest neighbor). However, existing methods face two key limitations in high-dimensional spaces. First, nearby vectors often do not belong to the query's true RkNN set, resulting in excessive candidate expansion overhead. Second, existing methods"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03225","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.03225/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.03225","created_at":"2026-06-03T01:05:35.363635+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03225v1","created_at":"2026-06-03T01:05:35.363635+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03225","created_at":"2026-06-03T01:05:35.363635+00:00"},{"alias_kind":"pith_short_12","alias_value":"FVL2SRTPHQY6","created_at":"2026-06-03T01:05:35.363635+00:00"},{"alias_kind":"pith_short_16","alias_value":"FVL2SRTPHQY6PHXQ","created_at":"2026-06-03T01:05:35.363635+00:00"},{"alias_kind":"pith_short_8","alias_value":"FVL2SRTP","created_at":"2026-06-03T01:05:35.363635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK","json":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK.json","graph_json":"https://pith.science/api/pith-number/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/graph.json","events_json":"https://pith.science/api/pith-number/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/events.json","paper":"https://pith.science/paper/FVL2SRTP"},"agent_actions":{"view_html":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK","download_json":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK.json","view_paper":"https://pith.science/paper/FVL2SRTP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03225&json=true","fetch_graph":"https://pith.science/api/pith-number/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/graph.json","fetch_events":"https://pith.science/api/pith-number/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/action/storage_attestation","attest_author":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/action/author_attestation","sign_citation":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/action/citation_signature","submit_replication":"https://pith.science/pith/FVL2SRTPHQY6PHXQ2RH4ZTJJTK/action/replication_record"}},"created_at":"2026-06-03T01:05:35.363635+00:00","updated_at":"2026-06-03T01:05:35.363635+00:00"}