{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4CUO2FGBAH2XGNBKKPBQ5ANWAD","short_pith_number":"pith:4CUO2FGB","schema_version":"1.0","canonical_sha256":"e0a8ed14c101f573342a53c30e81b600f058e55c2bed17f4935d91e89b04632a","source":{"kind":"arxiv","id":"2312.16760","version":1},"attestation_state":"computed","paper":{"title":"The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SE"],"primary_cat":"cs.LG","authors_text":"Changliu Liu, Christopher Brix, Stanley Bak, Taylor T. Johnson","submitted_at":"2023-12-28T00:46:35Z","abstract_excerpt":"This report summarizes the 4th International Verification of Neural Networks Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), that was collocated with the 35th 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 s"},"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":"2312.16760","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-28T00:46:35Z","cross_cats_sorted":["cs.AI","cs.SE"],"title_canon_sha256":"c61921b3c4ef5dad34cd324360027cb6f122304c3859654d7528c2370ffc7756","abstract_canon_sha256":"269d2c52a082a3e5c5d6bedd9fca4af9a8fa16d5b50e97169fe73daee5f7b7d7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:34.922309Z","signature_b64":"Xtv6TT7CCCS0AFPGsKRkO1mZRBqmUa2WDTdVZupdd3l1bdGMRg/Fhp/h4AugGDJnYmh69/kY40KS7zLOy8ftCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0a8ed14c101f573342a53c30e81b600f058e55c2bed17f4935d91e89b04632a","last_reissued_at":"2026-07-05T07:28:34.921827Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:34.921827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SE"],"primary_cat":"cs.LG","authors_text":"Changliu Liu, Christopher Brix, Stanley Bak, Taylor T. Johnson","submitted_at":"2023-12-28T00:46:35Z","abstract_excerpt":"This report summarizes the 4th International Verification of Neural Networks Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), that was collocated with the 35th 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 s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.16760","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/2312.16760/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":"2312.16760","created_at":"2026-07-05T07:28:34.921884+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.16760v1","created_at":"2026-07-05T07:28:34.921884+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.16760","created_at":"2026-07-05T07:28:34.921884+00:00"},{"alias_kind":"pith_short_12","alias_value":"4CUO2FGBAH2X","created_at":"2026-07-05T07:28:34.921884+00:00"},{"alias_kind":"pith_short_16","alias_value":"4CUO2FGBAH2XGNBK","created_at":"2026-07-05T07:28:34.921884+00:00"},{"alias_kind":"pith_short_8","alias_value":"4CUO2FGB","created_at":"2026-07-05T07:28:34.921884+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20292","citing_title":"Shifting-based Optimizable Linear Relaxations for General Activation Functions","ref_index":35,"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":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13845","citing_title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14972","citing_title":"Viverra: Text-to-Code with Guarantees","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13845","citing_title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04914","citing_title":"Analyzing Symbolic Properties for DRL Agents in Systems and Networking","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD","json":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD.json","graph_json":"https://pith.science/api/pith-number/4CUO2FGBAH2XGNBKKPBQ5ANWAD/graph.json","events_json":"https://pith.science/api/pith-number/4CUO2FGBAH2XGNBKKPBQ5ANWAD/events.json","paper":"https://pith.science/paper/4CUO2FGB"},"agent_actions":{"view_html":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD","download_json":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD.json","view_paper":"https://pith.science/paper/4CUO2FGB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.16760&json=true","fetch_graph":"https://pith.science/api/pith-number/4CUO2FGBAH2XGNBKKPBQ5ANWAD/graph.json","fetch_events":"https://pith.science/api/pith-number/4CUO2FGBAH2XGNBKKPBQ5ANWAD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD/action/storage_attestation","attest_author":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD/action/author_attestation","sign_citation":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD/action/citation_signature","submit_replication":"https://pith.science/pith/4CUO2FGBAH2XGNBKKPBQ5ANWAD/action/replication_record"}},"created_at":"2026-07-05T07:28:34.921884+00:00","updated_at":"2026-07-05T07:28:34.921884+00:00"}