{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:Z2E4BQMRONGGIYTKZH35H6AN5U","short_pith_number":"pith:Z2E4BQMR","schema_version":"1.0","canonical_sha256":"ce89c0c191734c64626ac9f7d3f80ded3beb7e037ab7f94c9c7d9ab3a0e4fd57","source":{"kind":"arxiv","id":"1702.01135","version":2},"attestation_state":"computed","paper":{"title":"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.AI","authors_text":"Clark Barrett, David Dill, Guy Katz, Kyle Julian, Mykel Kochenderfer","submitted_at":"2017-02-03T19:26:01Z","abstract_excerpt":"Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verific"},"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":"1702.01135","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-02-03T19:26:01Z","cross_cats_sorted":["cs.LO"],"title_canon_sha256":"7eb0a7ace4994afdbe54f0dcc06f755f2957d19b019ffaff07bb6099737aa063","abstract_canon_sha256":"2e0db536d2e838c66d439dac32004f20cb34e86b0e0f2141f251a41d928889a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:13.403014Z","signature_b64":"lJkTsDyPBh/js9Jvvp757BpRA5ccD89skRDtWMRXJIbAtpQGQV+9a41w/ze/Aki8FlZS6YlruOtoC2RiaNz7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce89c0c191734c64626ac9f7d3f80ded3beb7e037ab7f94c9c7d9ab3a0e4fd57","last_reissued_at":"2026-05-18T00:44:13.402563Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:13.402563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.AI","authors_text":"Clark Barrett, David Dill, Guy Katz, Kyle Julian, Mykel Kochenderfer","submitted_at":"2017-02-03T19:26:01Z","abstract_excerpt":"Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.01135","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":""},"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":"1702.01135","created_at":"2026-05-18T00:44:13.402637+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.01135v2","created_at":"2026-05-18T00:44:13.402637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.01135","created_at":"2026-05-18T00:44:13.402637+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2E4BQMRONGG","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2E4BQMRONGGIYTK","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2E4BQMR","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"1906.01820","citing_title":"Risks from Learned Optimization in Advanced Machine Learning Systems","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13845","citing_title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"2603.21991","citing_title":"$\\lambda$-GELU: Learning Gating Hardness for Controlled ReLU-ization in Deep Networks","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13845","citing_title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10396","citing_title":"Causal Explanations from the Geometric Properties of ReLU Neural Networks","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U","json":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U.json","graph_json":"https://pith.science/api/pith-number/Z2E4BQMRONGGIYTKZH35H6AN5U/graph.json","events_json":"https://pith.science/api/pith-number/Z2E4BQMRONGGIYTKZH35H6AN5U/events.json","paper":"https://pith.science/paper/Z2E4BQMR"},"agent_actions":{"view_html":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U","download_json":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U.json","view_paper":"https://pith.science/paper/Z2E4BQMR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.01135&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2E4BQMRONGGIYTKZH35H6AN5U/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2E4BQMRONGGIYTKZH35H6AN5U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U/action/storage_attestation","attest_author":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U/action/author_attestation","sign_citation":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U/action/citation_signature","submit_replication":"https://pith.science/pith/Z2E4BQMRONGGIYTKZH35H6AN5U/action/replication_record"}},"created_at":"2026-05-18T00:44:13.402637+00:00","updated_at":"2026-05-18T00:44:13.402637+00:00"}