{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AXPYNKLSIA6XQYEYWR7GN6IWST","short_pith_number":"pith:AXPYNKLS","schema_version":"1.0","canonical_sha256":"05df86a972403d786098b47e66f91694c0b784a41664cb1416123612da14e7dc","source":{"kind":"arxiv","id":"2405.11024","version":1},"attestation_state":"computed","paper":{"title":"GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amur Ghose, Didier Chetelat, Hui-Ling Zhen, Jianye Hao, Joseph Cotnareanu, Mark Coates, Mingxuan Yuan, Wenyi Xiao, Yingxue Zhang, Zhanguang Zhang","submitted_at":"2024-05-17T18:00:50Z","abstract_excerpt":"Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite grap"},"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":"2405.11024","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-17T18:00:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a8ea787c04422d4d486feb4c9f7e412ed6477a338c5eb13490522b6fb43d324d","abstract_canon_sha256":"ae9cd4dfa361d47f8e9a254a7b42d24f53018605e847f1e7bd23adbe9494d1cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:31.941519Z","signature_b64":"K2QlSh5mde0Oi0kxufJVvWH0Dg3hbB+7dQKInBE8YQjdElnHyNnNCaJscTX2KjjEywvRrRrTTvh2VT4YqjTBBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05df86a972403d786098b47e66f91694c0b784a41664cb1416123612da14e7dc","last_reissued_at":"2026-07-05T08:20:31.941027Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:31.941027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amur Ghose, Didier Chetelat, Hui-Ling Zhen, Jianye Hao, Joseph Cotnareanu, Mark Coates, Mingxuan Yuan, Wenyi Xiao, Yingxue Zhang, Zhanguang Zhang","submitted_at":"2024-05-17T18:00:50Z","abstract_excerpt":"Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite grap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11024","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/2405.11024/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":"2405.11024","created_at":"2026-07-05T08:20:31.941108+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.11024v1","created_at":"2026-07-05T08:20:31.941108+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11024","created_at":"2026-07-05T08:20:31.941108+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXPYNKLSIA6X","created_at":"2026-07-05T08:20:31.941108+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXPYNKLSIA6XQYEY","created_at":"2026-07-05T08:20:31.941108+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXPYNKLS","created_at":"2026-07-05T08:20:31.941108+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/AXPYNKLSIA6XQYEYWR7GN6IWST","json":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST.json","graph_json":"https://pith.science/api/pith-number/AXPYNKLSIA6XQYEYWR7GN6IWST/graph.json","events_json":"https://pith.science/api/pith-number/AXPYNKLSIA6XQYEYWR7GN6IWST/events.json","paper":"https://pith.science/paper/AXPYNKLS"},"agent_actions":{"view_html":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST","download_json":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST.json","view_paper":"https://pith.science/paper/AXPYNKLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.11024&json=true","fetch_graph":"https://pith.science/api/pith-number/AXPYNKLSIA6XQYEYWR7GN6IWST/graph.json","fetch_events":"https://pith.science/api/pith-number/AXPYNKLSIA6XQYEYWR7GN6IWST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST/action/storage_attestation","attest_author":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST/action/author_attestation","sign_citation":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST/action/citation_signature","submit_replication":"https://pith.science/pith/AXPYNKLSIA6XQYEYWR7GN6IWST/action/replication_record"}},"created_at":"2026-07-05T08:20:31.941108+00:00","updated_at":"2026-07-05T08:20:31.941108+00:00"}