{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:U2UFVBIRM4TUM2MLA3CSUPHPL3","short_pith_number":"pith:U2UFVBIR","schema_version":"1.0","canonical_sha256":"a6a85a8511672746698b06c52a3cef5ed9c677837bc063f523d0e3388da7c3c0","source":{"kind":"arxiv","id":"2601.07048","version":4},"attestation_state":"computed","paper":{"title":"GPU-Accelerated ANNS: Quantized for Speed, Built for Change","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DB","authors_text":"Hunter McCoy, Prashant Pandey, Zikun Wang","submitted_at":"2026-01-11T19:51:54Z","abstract_excerpt":"Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications. GPUs offer a promising path to high-performance ANNS: they provide massive parallelism for distance computations, are readily available, and can co-locate with downstream applications.\n  Despite these advantages, current GPU-accelerated ANNS systems face three key limitations. First, real-world applications operate on evolving datasets that require fast batch updates, yet most GPU indices must be rebuilt from scratch when new data arrives. Second, high-dimensional vectors st"},"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":"2601.07048","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.DB","submitted_at":"2026-01-11T19:51:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7c37de3503748f8f7fdd6ba880dce0ce1ba6199f1d1059148abbe8a429ffc5dd","abstract_canon_sha256":"3e926923999799e35e7d0b477b47a1038567be04023b6faf07e9506f7adb8b44"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T00:19:46.059550Z","signature_b64":"Ue2RxUQ7rVsjmc7l8+4S7t1YXv5ou+a6Mtvd0IEd/J6uxbwXXcn6n3L6XMaBl4ol8jh+ZzQqufCoZBs/pS+4CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6a85a8511672746698b06c52a3cef5ed9c677837bc063f523d0e3388da7c3c0","last_reissued_at":"2026-07-27T00:19:46.058658Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T00:19:46.058658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GPU-Accelerated ANNS: Quantized for Speed, Built for Change","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DB","authors_text":"Hunter McCoy, Prashant Pandey, Zikun Wang","submitted_at":"2026-01-11T19:51:54Z","abstract_excerpt":"Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications. GPUs offer a promising path to high-performance ANNS: they provide massive parallelism for distance computations, are readily available, and can co-locate with downstream applications.\n  Despite these advantages, current GPU-accelerated ANNS systems face three key limitations. First, real-world applications operate on evolving datasets that require fast batch updates, yet most GPU indices must be rebuilt from scratch when new data arrives. Second, high-dimensional vectors st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.07048","kind":"arxiv","version":4},"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/2601.07048/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":"2601.07048","created_at":"2026-07-27T00:19:46.059067+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.07048v4","created_at":"2026-07-27T00:19:46.059067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.07048","created_at":"2026-07-27T00:19:46.059067+00:00"},{"alias_kind":"pith_short_12","alias_value":"U2UFVBIRM4TU","created_at":"2026-07-27T00:19:46.059067+00:00"},{"alias_kind":"pith_short_16","alias_value":"U2UFVBIRM4TUM2ML","created_at":"2026-07-27T00:19:46.059067+00:00"},{"alias_kind":"pith_short_8","alias_value":"U2UFVBIR","created_at":"2026-07-27T00:19:46.059067+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.27090","citing_title":"InferScale: GPU-Native KV Injection for Personalized LLM Serving","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3","json":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3.json","graph_json":"https://pith.science/api/pith-number/U2UFVBIRM4TUM2MLA3CSUPHPL3/graph.json","events_json":"https://pith.science/api/pith-number/U2UFVBIRM4TUM2MLA3CSUPHPL3/events.json","paper":"https://pith.science/paper/U2UFVBIR"},"agent_actions":{"view_html":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3","download_json":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3.json","view_paper":"https://pith.science/paper/U2UFVBIR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.07048&json=true","fetch_graph":"https://pith.science/api/pith-number/U2UFVBIRM4TUM2MLA3CSUPHPL3/graph.json","fetch_events":"https://pith.science/api/pith-number/U2UFVBIRM4TUM2MLA3CSUPHPL3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3/action/storage_attestation","attest_author":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3/action/author_attestation","sign_citation":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3/action/citation_signature","submit_replication":"https://pith.science/pith/U2UFVBIRM4TUM2MLA3CSUPHPL3/action/replication_record"}},"created_at":"2026-07-27T00:19:46.059067+00:00","updated_at":"2026-07-27T00:19:46.059067+00:00"}