{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NPQCZGL5TU4F5YHJH3RSXE7HKM","short_pith_number":"pith:NPQCZGL5","canonical_record":{"source":{"id":"2503.17911","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2025-03-23T03:16:50Z","cross_cats_sorted":[],"title_canon_sha256":"86d771e8b3003ecd7be682f376b50536d3841e17d0fedad82531b9d3f5b9c906","abstract_canon_sha256":"0d4e4a3ea0bae361acf0d1c7603d5633d4ff3f33ddaf58dfce964ba15d957b59"},"schema_version":"1.0"},"canonical_sha256":"6be02c997d9d385ee0e93ee32b93e7531a02afe1b73823eb1542ccf659549e86","source":{"kind":"arxiv","id":"2503.17911","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.17911","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"arxiv_version","alias_value":"2503.17911v3","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17911","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"pith_short_12","alias_value":"NPQCZGL5TU4F","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"pith_short_16","alias_value":"NPQCZGL5TU4F5YHJ","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"pith_short_8","alias_value":"NPQCZGL5","created_at":"2026-07-05T11:37:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NPQCZGL5TU4F5YHJH3RSXE7HKM","target":"record","payload":{"canonical_record":{"source":{"id":"2503.17911","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2025-03-23T03:16:50Z","cross_cats_sorted":[],"title_canon_sha256":"86d771e8b3003ecd7be682f376b50536d3841e17d0fedad82531b9d3f5b9c906","abstract_canon_sha256":"0d4e4a3ea0bae361acf0d1c7603d5633d4ff3f33ddaf58dfce964ba15d957b59"},"schema_version":"1.0"},"canonical_sha256":"6be02c997d9d385ee0e93ee32b93e7531a02afe1b73823eb1542ccf659549e86","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:17.175199Z","signature_b64":"oDThrVMf7KVVCdHZZGfuCWern9ouNwcuJqnYeiVgNL8BvjxG/Q89Z3p/231AtVfzUYHPqXZGmcRd5n/3lHigCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6be02c997d9d385ee0e93ee32b93e7531a02afe1b73823eb1542ccf659549e86","last_reissued_at":"2026-07-05T11:37:17.174615Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:17.174615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.17911","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:37:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vlNlgvbY9SP/NeSAH3IO3gPzMxKFL0X21zcLxYrISY13uQo8iiRGMq8amujm7I1TCmRHUTTUJZVrDJKuzJvBAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T15:44:45.244107Z"},"content_sha256":"26f86a892d89cca8c95f9013b7e0b009acff392bc165ba58e2603629c9831f42","schema_version":"1.0","event_id":"sha256:26f86a892d89cca8c95f9013b7e0b009acff392bc165ba58e2603629c9831f42"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NPQCZGL5TU4F5YHJH3RSXE7HKM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Deming Chu, George Gu, Haotian Li, Heng Tao Shen, Jiabao Jin, Jingkuan Song, Mingyu Yang, Peng Cheng, Wei Jia, Xiangyu Wang, Xiaoyao Zhong, Xuemin Lin, Yi Xie, Zhitao Shen","submitted_at":"2025-03-23T03:16:50Z","abstract_excerpt":"Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accuracy with practical efficiency. Despite the advancements, these algorithms still face performance bottlenecks in production, due to the random memory access patterns of graph-based search and the high computational overheads of vector distance. In addition, the performance of a graph-based ANNS algorithm is highly sensitive to parameters, while selecting the optimal parameters is cost-prohibitive, e.g., manual tuning re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17911","kind":"arxiv","version":3},"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/2503.17911/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:37:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VtqTlkZTYp55L1lM3QPL0XFcC8zlgREPHxRzHrxfEgcgmqcNJOrX09KqtQeFnxBpsqFzduIpbyucY2PMXvGJCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T15:44:45.244646Z"},"content_sha256":"e1c055aeeda7b7c448c4f3e57cd65aa0de5ad27c87f7fa32627cd8de9fdce2b5","schema_version":"1.0","event_id":"sha256:e1c055aeeda7b7c448c4f3e57cd65aa0de5ad27c87f7fa32627cd8de9fdce2b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM/bundle.json","state_url":"https://pith.science/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-14T15:44:45Z","links":{"resolver":"https://pith.science/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM","bundle":"https://pith.science/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM/bundle.json","state":"https://pith.science/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NPQCZGL5TU4F5YHJH3RSXE7HKM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NPQCZGL5TU4F5YHJH3RSXE7HKM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0d4e4a3ea0bae361acf0d1c7603d5633d4ff3f33ddaf58dfce964ba15d957b59","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2025-03-23T03:16:50Z","title_canon_sha256":"86d771e8b3003ecd7be682f376b50536d3841e17d0fedad82531b9d3f5b9c906"},"schema_version":"1.0","source":{"id":"2503.17911","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.17911","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"arxiv_version","alias_value":"2503.17911v3","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17911","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"pith_short_12","alias_value":"NPQCZGL5TU4F","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"pith_short_16","alias_value":"NPQCZGL5TU4F5YHJ","created_at":"2026-07-05T11:37:17Z"},{"alias_kind":"pith_short_8","alias_value":"NPQCZGL5","created_at":"2026-07-05T11:37:17Z"}],"graph_snapshots":[{"event_id":"sha256:e1c055aeeda7b7c448c4f3e57cd65aa0de5ad27c87f7fa32627cd8de9fdce2b5","target":"graph","created_at":"2026-07-05T11:37:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.17911/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accuracy with practical efficiency. Despite the advancements, these algorithms still face performance bottlenecks in production, due to the random memory access patterns of graph-based search and the high computational overheads of vector distance. In addition, the performance of a graph-based ANNS algorithm is highly sensitive to parameters, while selecting the optimal parameters is cost-prohibitive, e.g., manual tuning re","authors_text":"Deming Chu, George Gu, Haotian Li, Heng Tao Shen, Jiabao Jin, Jingkuan Song, Mingyu Yang, Peng Cheng, Wei Jia, Xiangyu Wang, Xiaoyao Zhong, Xuemin Lin, Yi Xie, Zhitao Shen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2025-03-23T03:16:50Z","title":"VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17911","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:26f86a892d89cca8c95f9013b7e0b009acff392bc165ba58e2603629c9831f42","target":"record","created_at":"2026-07-05T11:37:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0d4e4a3ea0bae361acf0d1c7603d5633d4ff3f33ddaf58dfce964ba15d957b59","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2025-03-23T03:16:50Z","title_canon_sha256":"86d771e8b3003ecd7be682f376b50536d3841e17d0fedad82531b9d3f5b9c906"},"schema_version":"1.0","source":{"id":"2503.17911","kind":"arxiv","version":3}},"canonical_sha256":"6be02c997d9d385ee0e93ee32b93e7531a02afe1b73823eb1542ccf659549e86","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6be02c997d9d385ee0e93ee32b93e7531a02afe1b73823eb1542ccf659549e86","first_computed_at":"2026-07-05T11:37:17.174615Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:37:17.174615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oDThrVMf7KVVCdHZZGfuCWern9ouNwcuJqnYeiVgNL8BvjxG/Q89Z3p/231AtVfzUYHPqXZGmcRd5n/3lHigCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:37:17.175199Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.17911","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:26f86a892d89cca8c95f9013b7e0b009acff392bc165ba58e2603629c9831f42","sha256:e1c055aeeda7b7c448c4f3e57cd65aa0de5ad27c87f7fa32627cd8de9fdce2b5"],"state_sha256":"d7ae6179a0934b9f37ddb8e6a42d8bea7205f36d497ce16e9215568d1eeb9d22"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WuNdoYw+mHlJOb7pRFJO/tb7f0GRavcFEwn7fwybM+qCPS1MEv4r9+svcxrBwkQ0TZC8KAKEBBVXFk/+aZMdCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T15:44:45.300284Z","bundle_sha256":"197ab6ef44e29e2086afcbbd898720ecada43be157552b95d868f3e1aaa5f3df"}}