{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW","short_pith_number":"pith:PUN3W7ZM","schema_version":"1.0","canonical_sha256":"7d1bbb7f2c4eb3920b797be3c68eaeb58bda08eb1f1d2cdf32289129e5a95f10","source":{"kind":"arxiv","id":"2405.10611","version":2},"attestation_state":"computed","paper":{"title":"A Certified Proof Checker for Deep Neural Network Verification in Imandra","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.PL"],"primary_cat":"cs.LO","authors_text":"Ekaterina Komendantskaya, Grant Passmore, Guy Katz, Kathrin Stark, Omri Isac, Remi Desmartin","submitted_at":"2024-05-17T08:16:32Z","abstract_excerpt":"Recent advances in the verification of deep neural networks (DNNs) have opened the way for a broader usage of DNN verification technology in many application areas, including safety-critical ones. However, DNN verifiers are themselves complex programs that have been shown to be susceptible to errors and numerical imprecision; this, in turn, has raised the question of trust in DNN verifiers. One prominent attempt to address this issue is enhancing DNN verifiers with the capability of producing certificates of their results that are subject to independent algorithmic checking. While formulations"},"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":"2405.10611","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LO","submitted_at":"2024-05-17T08:16:32Z","cross_cats_sorted":["cs.AI","cs.PL"],"title_canon_sha256":"9ea6d13017d3bbb5dbea579c38ec15556f8141d3eabb346c5f2ad9a015218ca0","abstract_canon_sha256":"686cac9f6b8fcb985bab1010233c4c4228d0a0381d930e52643f7752acc89888"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:22.511742Z","signature_b64":"GHgwXAtekG3yMVP56gPE5YpMGXj8eHgJSg9gCeCtoHeUwBsZx0PLCHh5TQO4fFTgwE03W0hQetUXRewnq/zLBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d1bbb7f2c4eb3920b797be3c68eaeb58bda08eb1f1d2cdf32289129e5a95f10","last_reissued_at":"2026-07-05T11:26:22.511298Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:22.511298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Certified Proof Checker for Deep Neural Network Verification in Imandra","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.PL"],"primary_cat":"cs.LO","authors_text":"Ekaterina Komendantskaya, Grant Passmore, Guy Katz, Kathrin Stark, Omri Isac, Remi Desmartin","submitted_at":"2024-05-17T08:16:32Z","abstract_excerpt":"Recent advances in the verification of deep neural networks (DNNs) have opened the way for a broader usage of DNN verification technology in many application areas, including safety-critical ones. However, DNN verifiers are themselves complex programs that have been shown to be susceptible to errors and numerical imprecision; this, in turn, has raised the question of trust in DNN verifiers. One prominent attempt to address this issue is enhancing DNN verifiers with the capability of producing certificates of their results that are subject to independent algorithmic checking. While formulations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10611","kind":"arxiv","version":2},"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/2405.10611/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":"2405.10611","created_at":"2026-07-05T11:26:22.511384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.10611v2","created_at":"2026-07-05T11:26:22.511384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10611","created_at":"2026-07-05T11:26:22.511384+00:00"},{"alias_kind":"pith_short_12","alias_value":"PUN3W7ZMJ2ZZ","created_at":"2026-07-05T11:26:22.511384+00:00"},{"alias_kind":"pith_short_16","alias_value":"PUN3W7ZMJ2ZZEC3Z","created_at":"2026-07-05T11:26:22.511384+00:00"},{"alias_kind":"pith_short_8","alias_value":"PUN3W7ZM","created_at":"2026-07-05T11:26:22.511384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.05867","citing_title":"Neural Network Verification is a Programming Language Challenge","ref_index":37,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW","json":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW.json","graph_json":"https://pith.science/api/pith-number/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/graph.json","events_json":"https://pith.science/api/pith-number/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/events.json","paper":"https://pith.science/paper/PUN3W7ZM"},"agent_actions":{"view_html":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW","download_json":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW.json","view_paper":"https://pith.science/paper/PUN3W7ZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.10611&json=true","fetch_graph":"https://pith.science/api/pith-number/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/graph.json","fetch_events":"https://pith.science/api/pith-number/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/action/storage_attestation","attest_author":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/action/author_attestation","sign_citation":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/action/citation_signature","submit_replication":"https://pith.science/pith/PUN3W7ZMJ2ZZEC3ZPPR4NDVOWW/action/replication_record"}},"created_at":"2026-07-05T11:26:22.511384+00:00","updated_at":"2026-07-05T11:26:22.511384+00:00"}