{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TSZMRN2E5RBOKBQVHABM45GQ3I","short_pith_number":"pith:TSZMRN2E","schema_version":"1.0","canonical_sha256":"9cb2c8b744ec42e506153802ce74d0da20eea7b359753f3443448b1954cfae91","source":{"kind":"arxiv","id":"2505.15636","version":1},"attestation_state":"computed","paper":{"title":"Distance Adaptive Beam Search for Provably Accurate Graph-Based Nearest Neighbor Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB","cs.DS","cs.LG"],"primary_cat":"cs.IR","authors_text":"Cameron Musco, Christopher Musco, Haya Diwan, Jinrui Gou, Torsten Suel, Yousef Al-Jazzazi","submitted_at":"2025-05-21T15:18:53Z","abstract_excerpt":"Nearest neighbor search is central in machine learning, information retrieval, and databases. For high-dimensional datasets, graph-based methods such as HNSW, DiskANN, and NSG have become popular thanks to their empirical accuracy and efficiency. These methods construct a directed graph over the dataset and perform beam search on the graph to find nodes close to a given query. While significant work has focused on practical refinements and theoretical understanding of graph-based methods, many questions remain. We propose a new distance-based termination condition for beam search to replace th"},"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":"2505.15636","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-05-21T15:18:53Z","cross_cats_sorted":["cs.DB","cs.DS","cs.LG"],"title_canon_sha256":"ade3f839da88e75bb148c3864b1a8fe342870d66ab0debbc203f6eb7f87a821a","abstract_canon_sha256":"89ed4b97359c304cfd227cfce2e51ff69d1fe574670cd275d087f2dbf4757642"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:06:48.803512Z","signature_b64":"pK0FDiRBn9V9CFPdU8K9TLU1PLXT2kSbReUQsTmikrhEjtAcmjTgsenlj7jyJynl5mtTIN6ijejP/f4YBvZmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cb2c8b744ec42e506153802ce74d0da20eea7b359753f3443448b1954cfae91","last_reissued_at":"2026-07-05T11:06:48.802929Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:06:48.802929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distance Adaptive Beam Search for Provably Accurate Graph-Based Nearest Neighbor Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB","cs.DS","cs.LG"],"primary_cat":"cs.IR","authors_text":"Cameron Musco, Christopher Musco, Haya Diwan, Jinrui Gou, Torsten Suel, Yousef Al-Jazzazi","submitted_at":"2025-05-21T15:18:53Z","abstract_excerpt":"Nearest neighbor search is central in machine learning, information retrieval, and databases. For high-dimensional datasets, graph-based methods such as HNSW, DiskANN, and NSG have become popular thanks to their empirical accuracy and efficiency. These methods construct a directed graph over the dataset and perform beam search on the graph to find nodes close to a given query. While significant work has focused on practical refinements and theoretical understanding of graph-based methods, many questions remain. We propose a new distance-based termination condition for beam search to replace th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15636","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/2505.15636/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":"2505.15636","created_at":"2026-07-05T11:06:48.803000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.15636v1","created_at":"2026-07-05T11:06:48.803000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15636","created_at":"2026-07-05T11:06:48.803000+00:00"},{"alias_kind":"pith_short_12","alias_value":"TSZMRN2E5RBO","created_at":"2026-07-05T11:06:48.803000+00:00"},{"alias_kind":"pith_short_16","alias_value":"TSZMRN2E5RBOKBQV","created_at":"2026-07-05T11:06:48.803000+00:00"},{"alias_kind":"pith_short_8","alias_value":"TSZMRN2E","created_at":"2026-07-05T11:06:48.803000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.02338","citing_title":"HNSW with Accuracy Guarantees Using Graph Spanners","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I","json":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I.json","graph_json":"https://pith.science/api/pith-number/TSZMRN2E5RBOKBQVHABM45GQ3I/graph.json","events_json":"https://pith.science/api/pith-number/TSZMRN2E5RBOKBQVHABM45GQ3I/events.json","paper":"https://pith.science/paper/TSZMRN2E"},"agent_actions":{"view_html":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I","download_json":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I.json","view_paper":"https://pith.science/paper/TSZMRN2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.15636&json=true","fetch_graph":"https://pith.science/api/pith-number/TSZMRN2E5RBOKBQVHABM45GQ3I/graph.json","fetch_events":"https://pith.science/api/pith-number/TSZMRN2E5RBOKBQVHABM45GQ3I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I/action/storage_attestation","attest_author":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I/action/author_attestation","sign_citation":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I/action/citation_signature","submit_replication":"https://pith.science/pith/TSZMRN2E5RBOKBQVHABM45GQ3I/action/replication_record"}},"created_at":"2026-07-05T11:06:48.803000+00:00","updated_at":"2026-07-05T11:06:48.803000+00:00"}