{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3H2ZBLMSR2R76GLHOPHJ4PMCXO","short_pith_number":"pith:3H2ZBLMS","schema_version":"1.0","canonical_sha256":"d9f590ad928ea3ff196773ce9e3d82bbad92a1a2f36112e1abd405e96e8ce3d3","source":{"kind":"arxiv","id":"2305.18575","version":1},"attestation_state":"computed","paper":{"title":"Search-Based Regular Expression Inference on a GPU","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PL","authors_text":"Martin Berger, Mojtaba Valizadeh","submitted_at":"2023-05-29T19:37:15Z","abstract_excerpt":"Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. C"},"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":"2305.18575","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.PL","submitted_at":"2023-05-29T19:37:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7c10d81ff069d0522e421c5fd3f416394b7af0c6206394fd6dc208fccb723263","abstract_canon_sha256":"3a6280a179f1415444d01338f1cf4f36963ca4ab4873515a8c6093c7e7969f7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:15:02.364877Z","signature_b64":"Tknbaf3cS+WKyS/Q76dSdnlE/leelqSNLH6g0Oj5tkJ6lzrM/LYRSI2VliNZiqMA2AUS2+b/Oj3+YBQ1b2A0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9f590ad928ea3ff196773ce9e3d82bbad92a1a2f36112e1abd405e96e8ce3d3","last_reissued_at":"2026-07-05T06:15:02.364451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:15:02.364451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Search-Based Regular Expression Inference on a GPU","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PL","authors_text":"Martin Berger, Mojtaba Valizadeh","submitted_at":"2023-05-29T19:37:15Z","abstract_excerpt":"Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18575","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/2305.18575/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":"2305.18575","created_at":"2026-07-05T06:15:02.364523+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.18575v1","created_at":"2026-07-05T06:15:02.364523+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18575","created_at":"2026-07-05T06:15:02.364523+00:00"},{"alias_kind":"pith_short_12","alias_value":"3H2ZBLMSR2R7","created_at":"2026-07-05T06:15:02.364523+00:00"},{"alias_kind":"pith_short_16","alias_value":"3H2ZBLMSR2R76GLH","created_at":"2026-07-05T06:15:02.364523+00:00"},{"alias_kind":"pith_short_8","alias_value":"3H2ZBLMS","created_at":"2026-07-05T06:15:02.364523+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/3H2ZBLMSR2R76GLHOPHJ4PMCXO","json":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO.json","graph_json":"https://pith.science/api/pith-number/3H2ZBLMSR2R76GLHOPHJ4PMCXO/graph.json","events_json":"https://pith.science/api/pith-number/3H2ZBLMSR2R76GLHOPHJ4PMCXO/events.json","paper":"https://pith.science/paper/3H2ZBLMS"},"agent_actions":{"view_html":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO","download_json":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO.json","view_paper":"https://pith.science/paper/3H2ZBLMS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.18575&json=true","fetch_graph":"https://pith.science/api/pith-number/3H2ZBLMSR2R76GLHOPHJ4PMCXO/graph.json","fetch_events":"https://pith.science/api/pith-number/3H2ZBLMSR2R76GLHOPHJ4PMCXO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO/action/storage_attestation","attest_author":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO/action/author_attestation","sign_citation":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO/action/citation_signature","submit_replication":"https://pith.science/pith/3H2ZBLMSR2R76GLHOPHJ4PMCXO/action/replication_record"}},"created_at":"2026-07-05T06:15:02.364523+00:00","updated_at":"2026-07-05T06:15:02.364523+00:00"}