{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:PNJQDCN3SE5FHOV77BSZF4MHS6","short_pith_number":"pith:PNJQDCN3","schema_version":"1.0","canonical_sha256":"7b530189bb913a53babff86592f1879784e25b680623645eaf1a87d1581bbc43","source":{"kind":"arxiv","id":"1610.06371","version":4},"attestation_state":"computed","paper":{"title":"Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Cyrille Jegourel, Jingyi Wang, Jun Sun, Shengchao Qin","submitted_at":"2016-10-20T11:49:14Z","abstract_excerpt":"Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically `verify' such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture `enough' behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models"},"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":"1610.06371","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-10-20T11:49:14Z","cross_cats_sorted":[],"title_canon_sha256":"35996be7623f800a9d42d3f41fd1054b29fe1887afd3f6dc946fddd79c305bf1","abstract_canon_sha256":"ab7e994571188231825060d043a661166bfcd8d352aa058e099111e99e285cc7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:20:30.110164Z","signature_b64":"ITAQLeByDxtMrl/cmeMcB7quHycANIfCafYuKBXseNL4ayFqpPveFB5BA7PQrLki/+G7sDfuS8EKIAITC7faBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b530189bb913a53babff86592f1879784e25b680623645eaf1a87d1581bbc43","last_reissued_at":"2026-07-05T00:20:30.109647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:20:30.109647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Cyrille Jegourel, Jingyi Wang, Jun Sun, Shengchao Qin","submitted_at":"2016-10-20T11:49:14Z","abstract_excerpt":"Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically `verify' such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture `enough' behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.06371","kind":"arxiv","version":4},"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/1610.06371/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":"1610.06371","created_at":"2026-07-05T00:20:30.109699+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.06371v4","created_at":"2026-07-05T00:20:30.109699+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.06371","created_at":"2026-07-05T00:20:30.109699+00:00"},{"alias_kind":"pith_short_12","alias_value":"PNJQDCN3SE5F","created_at":"2026-07-05T00:20:30.109699+00:00"},{"alias_kind":"pith_short_16","alias_value":"PNJQDCN3SE5FHOV7","created_at":"2026-07-05T00:20:30.109699+00:00"},{"alias_kind":"pith_short_8","alias_value":"PNJQDCN3","created_at":"2026-07-05T00:20:30.109699+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/PNJQDCN3SE5FHOV77BSZF4MHS6","json":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6.json","graph_json":"https://pith.science/api/pith-number/PNJQDCN3SE5FHOV77BSZF4MHS6/graph.json","events_json":"https://pith.science/api/pith-number/PNJQDCN3SE5FHOV77BSZF4MHS6/events.json","paper":"https://pith.science/paper/PNJQDCN3"},"agent_actions":{"view_html":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6","download_json":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6.json","view_paper":"https://pith.science/paper/PNJQDCN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.06371&json=true","fetch_graph":"https://pith.science/api/pith-number/PNJQDCN3SE5FHOV77BSZF4MHS6/graph.json","fetch_events":"https://pith.science/api/pith-number/PNJQDCN3SE5FHOV77BSZF4MHS6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6/action/storage_attestation","attest_author":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6/action/author_attestation","sign_citation":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6/action/citation_signature","submit_replication":"https://pith.science/pith/PNJQDCN3SE5FHOV77BSZF4MHS6/action/replication_record"}},"created_at":"2026-07-05T00:20:30.109699+00:00","updated_at":"2026-07-05T00:20:30.109699+00:00"}