{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:64RLJO4CPCPS75JASMEDJIQBK4","short_pith_number":"pith:64RLJO4C","schema_version":"1.0","canonical_sha256":"f722b4bb82789f2ff520930834a2015715a43b54ad38a1882877e6adff3973b0","source":{"kind":"arxiv","id":"2207.11649","version":2},"attestation_state":"computed","paper":{"title":"OCTAL: Graph Representation Learning for LTL Model Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.FL","cs.LG"],"primary_cat":"cs.PL","authors_text":"Haoteng Yin, Prasita Mukherjee, Susheel Suresh, Tiark Rompf","submitted_at":"2022-07-24T03:34:21Z","abstract_excerpt":"Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from the state space explosion problem that makes them impractical for large scale systems and/or specifications. In this paper, we propose to use graph representation learning (GRL) for solving linear temporal logic (LTL) model checking, where the system and the specification are expressed by a B\\\"uchi automaton and an LTL formula respectively. A novel GRL-based framework OCTAL, is designed to learn the representation of "},"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":"2207.11649","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2022-07-24T03:34:21Z","cross_cats_sorted":["cs.FL","cs.LG"],"title_canon_sha256":"906146e66ec5f81df3b6be41a0fae5fc80599db3dd720d2c756f046d01ffb82c","abstract_canon_sha256":"b42b9224ac8ee515c7c97085c396eb1eca48d82a4e9f3154b114806142f3f079"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:43:21.490144Z","signature_b64":"1zidAE09RP82/tRVd93ozgkqrOIXqIQruN1UaSjcXJdg4W8Yofl+J9LQJEOPVgDESUsHKVmJillMdvbrZiIyCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f722b4bb82789f2ff520930834a2015715a43b54ad38a1882877e6adff3973b0","last_reissued_at":"2026-07-05T04:43:21.489750Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:43:21.489750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OCTAL: Graph Representation Learning for LTL Model Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.FL","cs.LG"],"primary_cat":"cs.PL","authors_text":"Haoteng Yin, Prasita Mukherjee, Susheel Suresh, Tiark Rompf","submitted_at":"2022-07-24T03:34:21Z","abstract_excerpt":"Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from the state space explosion problem that makes them impractical for large scale systems and/or specifications. In this paper, we propose to use graph representation learning (GRL) for solving linear temporal logic (LTL) model checking, where the system and the specification are expressed by a B\\\"uchi automaton and an LTL formula respectively. A novel GRL-based framework OCTAL, is designed to learn the representation of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.11649","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/2207.11649/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":"2207.11649","created_at":"2026-07-05T04:43:21.489806+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.11649v2","created_at":"2026-07-05T04:43:21.489806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.11649","created_at":"2026-07-05T04:43:21.489806+00:00"},{"alias_kind":"pith_short_12","alias_value":"64RLJO4CPCPS","created_at":"2026-07-05T04:43:21.489806+00:00"},{"alias_kind":"pith_short_16","alias_value":"64RLJO4CPCPS75JA","created_at":"2026-07-05T04:43:21.489806+00:00"},{"alias_kind":"pith_short_8","alias_value":"64RLJO4C","created_at":"2026-07-05T04:43:21.489806+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05106","citing_title":"A Neuro-Symbolic Framework for Sequence Classification with Relational and Temporal Knowledge","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4","json":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4.json","graph_json":"https://pith.science/api/pith-number/64RLJO4CPCPS75JASMEDJIQBK4/graph.json","events_json":"https://pith.science/api/pith-number/64RLJO4CPCPS75JASMEDJIQBK4/events.json","paper":"https://pith.science/paper/64RLJO4C"},"agent_actions":{"view_html":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4","download_json":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4.json","view_paper":"https://pith.science/paper/64RLJO4C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.11649&json=true","fetch_graph":"https://pith.science/api/pith-number/64RLJO4CPCPS75JASMEDJIQBK4/graph.json","fetch_events":"https://pith.science/api/pith-number/64RLJO4CPCPS75JASMEDJIQBK4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4/action/storage_attestation","attest_author":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4/action/author_attestation","sign_citation":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4/action/citation_signature","submit_replication":"https://pith.science/pith/64RLJO4CPCPS75JASMEDJIQBK4/action/replication_record"}},"created_at":"2026-07-05T04:43:21.489806+00:00","updated_at":"2026-07-05T04:43:21.489806+00:00"}