{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QBLB452DZ3PJISX53ZHLDG273I","short_pith_number":"pith:QBLB452D","schema_version":"1.0","canonical_sha256":"80561e7743cede944afdde4eb19b5fda186f5e8412c659d8033d5db38978a959","source":{"kind":"arxiv","id":"2412.19985","version":1},"attestation_state":"computed","paper":{"title":"The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Christopher Brix, Haoze Wu, Stanley Bak, Taylor T. Johnson","submitted_at":"2024-12-28T03:07:00Z","abstract_excerpt":"This report summarizes the 5th International Verification of Neural Networks Competition (VNN-COMP 2024), held as a part of the 7th International Symposium on AI Verification (SAIV), that was collocated with the 36th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LI"},"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":"2412.19985","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-28T03:07:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8bc4fd353461a2e49639526a66ef8fd048f7fc9379ec7fde782444ab3557f672","abstract_canon_sha256":"ac4816146d12064f43765ba3c7301deea6800dc48055226524ab8d66ab987612"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:52.693604Z","signature_b64":"3ROKOmSZDD2x/c+MH3DPdy2PF7DcdWrDIZ4fW6nm+m4vtqPDjqLpxFJi4UAyEQ48nkuHJu+RlnTc7z5LbrjbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80561e7743cede944afdde4eb19b5fda186f5e8412c659d8033d5db38978a959","last_reissued_at":"2026-07-05T09:54:52.693049Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:52.693049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Christopher Brix, Haoze Wu, Stanley Bak, Taylor T. Johnson","submitted_at":"2024-12-28T03:07:00Z","abstract_excerpt":"This report summarizes the 5th International Verification of Neural Networks Competition (VNN-COMP 2024), held as a part of the 7th International Symposium on AI Verification (SAIV), that was collocated with the 36th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LI"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19985","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/2412.19985/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":"2412.19985","created_at":"2026-07-05T09:54:52.693124+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19985v1","created_at":"2026-07-05T09:54:52.693124+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19985","created_at":"2026-07-05T09:54:52.693124+00:00"},{"alias_kind":"pith_short_12","alias_value":"QBLB452DZ3PJ","created_at":"2026-07-05T09:54:52.693124+00:00"},{"alias_kind":"pith_short_16","alias_value":"QBLB452DZ3PJISX5","created_at":"2026-07-05T09:54:52.693124+00:00"},{"alias_kind":"pith_short_8","alias_value":"QBLB452D","created_at":"2026-07-05T09:54:52.693124+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":12,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20292","citing_title":"Shifting-based Optimizable Linear Relaxations for General Activation Functions","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19532","citing_title":"Vancomycert: A Certified Neuro-Symbolic Drug Delivery System (Case Study)","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09377","citing_title":"Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25346","citing_title":"Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29537","citing_title":"The Complexity of Verifying Feedforward Neural Networks in Quantised Settings","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.25239","citing_title":"Tensor-Based Batch Fuzzing with Adaptive Perturbation Scaling for Deep Neural Networks","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2602.16473","citing_title":"Synthesis and Verification of Transformer Programs (Technical Report)","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13845","citing_title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14972","citing_title":"Viverra: Text-to-Code with Guarantees","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2603.23878","citing_title":"The Luna Bound Propagator for Formal Analysis of Neural Networks","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13845","citing_title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24379","citing_title":"Certified geometric robustness -- Super-DeepG","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I","json":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I.json","graph_json":"https://pith.science/api/pith-number/QBLB452DZ3PJISX53ZHLDG273I/graph.json","events_json":"https://pith.science/api/pith-number/QBLB452DZ3PJISX53ZHLDG273I/events.json","paper":"https://pith.science/paper/QBLB452D"},"agent_actions":{"view_html":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I","download_json":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I.json","view_paper":"https://pith.science/paper/QBLB452D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19985&json=true","fetch_graph":"https://pith.science/api/pith-number/QBLB452DZ3PJISX53ZHLDG273I/graph.json","fetch_events":"https://pith.science/api/pith-number/QBLB452DZ3PJISX53ZHLDG273I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I/action/storage_attestation","attest_author":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I/action/author_attestation","sign_citation":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I/action/citation_signature","submit_replication":"https://pith.science/pith/QBLB452DZ3PJISX53ZHLDG273I/action/replication_record"}},"created_at":"2026-07-05T09:54:52.693124+00:00","updated_at":"2026-07-05T09:54:52.693124+00:00"}