{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AXPYNKLSIA6XQYEYWR7GN6IWST","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ae9cd4dfa361d47f8e9a254a7b42d24f53018605e847f1e7bd23adbe9494d1cc","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-17T18:00:50Z","title_canon_sha256":"a8ea787c04422d4d486feb4c9f7e412ed6477a338c5eb13490522b6fb43d324d"},"schema_version":"1.0","source":{"id":"2405.11024","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.11024","created_at":"2026-07-05T08:20:31Z"},{"alias_kind":"arxiv_version","alias_value":"2405.11024v1","created_at":"2026-07-05T08:20:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11024","created_at":"2026-07-05T08:20:31Z"},{"alias_kind":"pith_short_12","alias_value":"AXPYNKLSIA6X","created_at":"2026-07-05T08:20:31Z"},{"alias_kind":"pith_short_16","alias_value":"AXPYNKLSIA6XQYEY","created_at":"2026-07-05T08:20:31Z"},{"alias_kind":"pith_short_8","alias_value":"AXPYNKLS","created_at":"2026-07-05T08:20:31Z"}],"graph_snapshots":[{"event_id":"sha256:ccbef176ee413288141fbd6abf0a787d4424a45bfc10189cbd8d10af931ac26a","target":"graph","created_at":"2026-07-05T08:20:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.11024/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Amur Ghose, Didier Chetelat, Hui-Ling Zhen, Jianye Hao, Joseph Cotnareanu, Mark Coates, Mingxuan Yuan, Wenyi Xiao, Yingxue Zhang, Zhanguang Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-17T18:00:50Z","title":"GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11024","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a060dbd2efa7761eaac52cba233e87ee905c75f5311454d05a424d2c74bd306e","target":"record","created_at":"2026-07-05T08:20:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ae9cd4dfa361d47f8e9a254a7b42d24f53018605e847f1e7bd23adbe9494d1cc","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-17T18:00:50Z","title_canon_sha256":"a8ea787c04422d4d486feb4c9f7e412ed6477a338c5eb13490522b6fb43d324d"},"schema_version":"1.0","source":{"id":"2405.11024","kind":"arxiv","version":1}},"canonical_sha256":"05df86a972403d786098b47e66f91694c0b784a41664cb1416123612da14e7dc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"05df86a972403d786098b47e66f91694c0b784a41664cb1416123612da14e7dc","first_computed_at":"2026-07-05T08:20:31.941027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:20:31.941027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K2QlSh5mde0Oi0kxufJVvWH0Dg3hbB+7dQKInBE8YQjdElnHyNnNCaJscTX2KjjEywvRrRrTTvh2VT4YqjTBBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:20:31.941519Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.11024","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a060dbd2efa7761eaac52cba233e87ee905c75f5311454d05a424d2c74bd306e","sha256:ccbef176ee413288141fbd6abf0a787d4424a45bfc10189cbd8d10af931ac26a"],"state_sha256":"33563b78994860953b6e04a28da0e05c07c9ea33f5575555daf2308ed72641f7"}