{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OLRKQI3SL6EMJH3KTGIDITFVMI","short_pith_number":"pith:OLRKQI3S","schema_version":"1.0","canonical_sha256":"72e2a823725f88c49f6a9990344cb5622c90fe265136bcf2da5c7d761bbcbf54","source":{"kind":"arxiv","id":"2406.14190","version":5},"attestation_state":"computed","paper":{"title":"Extended Resolution Clause Learning via Dual Implication Points","license":"http://creativecommons.org/licenses/by/4.0/","headline":"SAT solvers can learn stronger clauses by dynamically adding variables for dual implication points in the implication graph.","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Albert Oliveras, Jonathan Chung, Sam Buss, Vijay Ganesh","submitted_at":"2024-06-20T10:50:26Z","abstract_excerpt":"We present a new extended resolution clause learning (ERCL) algorithm, implemented as part of a conflict-driven clause-learning (CDCL) SAT solver, wherein new variables are dynamically introduced as definitions for {\\it Dual Implication Points} (DIPs) in the implication graph constructed by the solver at runtime. DIPs are generalizations of unique implication points and can be informally viewed as a pair of dominator nodes, from the decision variable at the highest decision level to the conflict node, in an implication graph. We perform extensive experimental evaluation to establish the effica"},"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":"2406.14190","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LO","submitted_at":"2024-06-20T10:50:26Z","cross_cats_sorted":[],"title_canon_sha256":"440f496df323feb6e7abb58a29a9008698476c51a8a514e44f8361936ef980df","abstract_canon_sha256":"741f0ed9104fbfc54d9fb336d2c5a254ddb1abf266d7c8e46520637c5a3096e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:01.001927Z","signature_b64":"R/RCUishXhlQyzXrv40oT4zGkQToFQvWcmX7teTX3EVEMzOl53fE3y3SYfig19PPirelZff/dDiu466EEYuSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72e2a823725f88c49f6a9990344cb5622c90fe265136bcf2da5c7d761bbcbf54","last_reissued_at":"2026-05-25T02:01:01.001261Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:01.001261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extended Resolution Clause Learning via Dual Implication Points","license":"http://creativecommons.org/licenses/by/4.0/","headline":"SAT solvers can learn stronger clauses by dynamically adding variables for dual implication points in the implication graph.","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Albert Oliveras, Jonathan Chung, Sam Buss, Vijay Ganesh","submitted_at":"2024-06-20T10:50:26Z","abstract_excerpt":"We present a new extended resolution clause learning (ERCL) algorithm, implemented as part of a conflict-driven clause-learning (CDCL) SAT solver, wherein new variables are dynamically introduced as definitions for {\\it Dual Implication Points} (DIPs) in the implication graph constructed by the solver at runtime. DIPs are generalizations of unique implication points and can be informally viewed as a pair of dominator nodes, from the decision variable at the highest decision level to the conflict node, in an implication graph. We perform extensive experimental evaluation to establish the effica"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We show that xMapleLCM outperforms these solvers on Tseitin and XORified formulas.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Dynamically introducing new variables for DIPs at runtime produces net benefit without prohibitive overhead or compromising solver correctness on the evaluated instance classes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"New ERCL method using dual implication points in CDCL solvers outperforms baselines on Tseitin and XORified formulas.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SAT solvers can learn stronger clauses by dynamically adding variables for dual implication points in the implication graph.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"130704422065b1489782fd2501859f9d4653533a02a7b1a64b2694ee414c7a99"},"source":{"id":"2406.14190","kind":"arxiv","version":5},"verdict":{"id":"b838e7e6-0d26-4bc9-9681-2937b15edd73","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-24T00:27:27.539389Z","strongest_claim":"We show that xMapleLCM outperforms these solvers on Tseitin and XORified formulas.","one_line_summary":"New ERCL method using dual implication points in CDCL solvers outperforms baselines on Tseitin and XORified formulas.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Dynamically introducing new variables for DIPs at runtime produces net benefit without prohibitive overhead or compromising solver correctness on the evaluated instance classes.","pith_extraction_headline":"SAT solvers can learn stronger clauses by dynamically adding variables for dual implication points in the implication graph."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.14190/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":"2406.14190","created_at":"2026-05-25T02:01:01.001342+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.14190v5","created_at":"2026-05-25T02:01:01.001342+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.14190","created_at":"2026-05-25T02:01:01.001342+00:00"},{"alias_kind":"pith_short_12","alias_value":"OLRKQI3SL6EM","created_at":"2026-05-25T02:01:01.001342+00:00"},{"alias_kind":"pith_short_16","alias_value":"OLRKQI3SL6EMJH3K","created_at":"2026-05-25T02:01:01.001342+00:00"},{"alias_kind":"pith_short_8","alias_value":"OLRKQI3S","created_at":"2026-05-25T02:01:01.001342+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/OLRKQI3SL6EMJH3KTGIDITFVMI","json":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI.json","graph_json":"https://pith.science/api/pith-number/OLRKQI3SL6EMJH3KTGIDITFVMI/graph.json","events_json":"https://pith.science/api/pith-number/OLRKQI3SL6EMJH3KTGIDITFVMI/events.json","paper":"https://pith.science/paper/OLRKQI3S"},"agent_actions":{"view_html":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI","download_json":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI.json","view_paper":"https://pith.science/paper/OLRKQI3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.14190&json=true","fetch_graph":"https://pith.science/api/pith-number/OLRKQI3SL6EMJH3KTGIDITFVMI/graph.json","fetch_events":"https://pith.science/api/pith-number/OLRKQI3SL6EMJH3KTGIDITFVMI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI/action/storage_attestation","attest_author":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI/action/author_attestation","sign_citation":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI/action/citation_signature","submit_replication":"https://pith.science/pith/OLRKQI3SL6EMJH3KTGIDITFVMI/action/replication_record"}},"created_at":"2026-05-25T02:01:01.001342+00:00","updated_at":"2026-05-25T02:01:01.001342+00:00"}