{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QMRHUWB7E5ZQRNK5ZOFVUX36IP","short_pith_number":"pith:QMRHUWB7","schema_version":"1.0","canonical_sha256":"83227a583f277308b55dcb8b5a5f7e43c7dbb307069df1b006f6526c850ad5bc","source":{"kind":"arxiv","id":"2507.17453","version":1},"attestation_state":"computed","paper":{"title":"Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PL","cs.SE"],"primary_cat":"cs.LG","authors_text":"Guanqin Zhang, H.M.N. Dilum Bandara, Jianjun Zhao, Kota Fukuda, Shiping Chen, Yulei Sui, Zhenya Zhang","submitted_at":"2025-07-23T12:20:20Z","abstract_excerpt":"The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the state-of-the-art, branch and bound (BaB) is a \"divide-and-conquer\" strategy that applies off-the-shelf verifiers to sub-problems for which they perform better. While BaB can identify the sub-problems that are necessary to be split, it explores the space of these sub-problems in a naive \"first-come-first-serve\" manner, thereby suffering from an issue of inefficiency to reach a verification conclusion. To bridge this "},"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":"2507.17453","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-23T12:20:20Z","cross_cats_sorted":["cs.PL","cs.SE"],"title_canon_sha256":"28e659ffddf150daf9b61e678cdb2e2c57a0efcba1d9c4367b5ef2ccdfa23611","abstract_canon_sha256":"d6ab88a3954f30cb1fe32eaf675266214aaa46cbc792cdb660fa6ca939b5d2f6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:08.083799Z","signature_b64":"J4Ly+4p2ySMspKRXpuhAouXZ02gWV/DjRN9LioxUnzCmFi5h10ZPHaUB3t9dKWaux7b9RUgRYUojpFY/er3RDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83227a583f277308b55dcb8b5a5f7e43c7dbb307069df1b006f6526c850ad5bc","last_reissued_at":"2026-07-05T11:42:08.083204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:08.083204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PL","cs.SE"],"primary_cat":"cs.LG","authors_text":"Guanqin Zhang, H.M.N. Dilum Bandara, Jianjun Zhao, Kota Fukuda, Shiping Chen, Yulei Sui, Zhenya Zhang","submitted_at":"2025-07-23T12:20:20Z","abstract_excerpt":"The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the state-of-the-art, branch and bound (BaB) is a \"divide-and-conquer\" strategy that applies off-the-shelf verifiers to sub-problems for which they perform better. While BaB can identify the sub-problems that are necessary to be split, it explores the space of these sub-problems in a naive \"first-come-first-serve\" manner, thereby suffering from an issue of inefficiency to reach a verification conclusion. To bridge this "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17453","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/2507.17453/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":"2507.17453","created_at":"2026-07-05T11:42:08.083272+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.17453v1","created_at":"2026-07-05T11:42:08.083272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17453","created_at":"2026-07-05T11:42:08.083272+00:00"},{"alias_kind":"pith_short_12","alias_value":"QMRHUWB7E5ZQ","created_at":"2026-07-05T11:42:08.083272+00:00"},{"alias_kind":"pith_short_16","alias_value":"QMRHUWB7E5ZQRNK5","created_at":"2026-07-05T11:42:08.083272+00:00"},{"alias_kind":"pith_short_8","alias_value":"QMRHUWB7","created_at":"2026-07-05T11:42:08.083272+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/QMRHUWB7E5ZQRNK5ZOFVUX36IP","json":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP.json","graph_json":"https://pith.science/api/pith-number/QMRHUWB7E5ZQRNK5ZOFVUX36IP/graph.json","events_json":"https://pith.science/api/pith-number/QMRHUWB7E5ZQRNK5ZOFVUX36IP/events.json","paper":"https://pith.science/paper/QMRHUWB7"},"agent_actions":{"view_html":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP","download_json":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP.json","view_paper":"https://pith.science/paper/QMRHUWB7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.17453&json=true","fetch_graph":"https://pith.science/api/pith-number/QMRHUWB7E5ZQRNK5ZOFVUX36IP/graph.json","fetch_events":"https://pith.science/api/pith-number/QMRHUWB7E5ZQRNK5ZOFVUX36IP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP/action/storage_attestation","attest_author":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP/action/author_attestation","sign_citation":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP/action/citation_signature","submit_replication":"https://pith.science/pith/QMRHUWB7E5ZQRNK5ZOFVUX36IP/action/replication_record"}},"created_at":"2026-07-05T11:42:08.083272+00:00","updated_at":"2026-07-05T11:42:08.083272+00:00"}