{"as_of":"2026-08-06T21:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3a5ed866d7f79ce506ab0263a24c2e76dbcc7f2c273a011c0937db0aa56cd502","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T06:23:10.906146Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2309.11452/citation-record","integrity":"/paper/2309.11452/integrity","json":"/paper/2309.11452/citation-record.json","paper":"/paper/2309.11452"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"54f6faec-7a9b-4207-a9e6-6890f87a18db","year":1971},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:5c4ae891e52a073473ea97a1269873826983981fb101eb7316e23dad7f713b1c","observation_id":"326610a2-f8d7-4594-ba60-50d1a2753fef","resolution":{"observed_at":"2026-05-24T06:24:00.687555Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"dfe61655-826e-41b1-9682-f27ce1aa7298","year":1973},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:9554825a96a5c7926a4e088e6be51c248b0af18c6ebb9818e6e7561c8baaa44b","observation_id":"3b5e75af-c688-42af-ae01-27dbcb5a4bc7","resolution":{"observed_at":"2026-05-24T06:24:00.678898Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Machine learning methods in solving the boolean satisfiability problem","venue":null,"work_id":"6b0e7e8a-111d-49bc-aa2f-c17112d4fdb2","year":2022},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:1e3f2adcb37e471dc87b8a7716f481d3f946c6f0aa518214eb4d44a749bd65c0","observation_id":"e5917f69-ed57-4fe3-8f68-2d9d626c34b9","resolution":{"observed_at":"2026-05-24T06:24:00.658906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph neural networks and boolean satisfiability","venue":null,"work_id":"3b607211-603f-40be-8265-36c5b686533c","year":2017},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:42a74e57e5d2a4e61a2b15814721c66e1f85de7833c2a3b845425e12a257318b","observation_id":"7925d1ae-7766-43fc-b4f6-0e51b0b08561","resolution":{"observed_at":"2026-05-24T06:24:00.676106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ee80c0a7-9b84-4414-950d-69f3c21f6aa4","year":2019},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:1f70bab4d6973bf4f58f82b9bc4e7a08bf7d4a5e2ab718f9e7bdfd0a703cb2aa","observation_id":"9d966ff1-ae17-429d-b399-8ed0dd27252f","resolution":{"observed_at":"2026-05-24T06:24:00.696869Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Goal- aware neural SAT solver","venue":null,"work_id":"c69374d9-575e-4e4e-8726-d4f84b2d868b","year":2022},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:725bc5e5d035727d4049c7ef3a7a72dbf4d76363cb4ca14dcaea48f986301667","observation_id":"cac74c38-ad7b-4eb6-82f4-ef6050012639","resolution":{"observed_at":"2026-05-24T06:24:00.656006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Guiding high-performance sat solvers with unsat-core predictions","venue":null,"work_id":"0b79ac09-6cd0-41b1-ad9a-a43f040f7b1b","year":2019},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:3853516044609c66116793119d513f0d6baae59bce4630133ce7b1605af2fd0e","observation_id":"36f6744f-c9dc-40e1-802e-5fc99fa392c2","resolution":{"observed_at":"2026-05-24T06:24:00.661680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural heuristics for sat solving","venue":null,"work_id":"fa352d1e-b098-472c-9962-ca6e93fbddc5","year":2020},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:beecc019a4f252c4a52fcaa0516f440451903929a547db46fbbea63b2650a079","observation_id":"49e4a0eb-d8e8-4aac-9081-41046a305f75","resolution":{"observed_at":"2026-05-24T06:24:00.649535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Enhancing sat solvers with glue variable predictions","venue":null,"work_id":"a3c41ae4-38c1-4bb7-b031-8d993e125984","year":2020},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:5dbdbfd67294ab80e7d87adf1b9386921ad4665cbc9660cbfc73a569455ab88b","observation_id":"f8037742-c609-4f3d-a949-541f12effb1c","resolution":{"observed_at":"2026-05-24T06:24:00.616584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning local search heuristics for boolean satisfiability","venue":null,"work_id":"218a8db3-c2f3-4045-b3b1-097d570e2a69","year":2019},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:dcd4db34bb4e54fa8922f1b20e350e53d76cfcebf8494e4902adfafc004c1c6e","observation_id":"08ea3e36-423f-4829-95fc-16dbe2bb8d49","resolution":{"observed_at":"2026-05-24T06:24:00.652730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"NLocalSAT: Boosting local search with solution prediction","venue":null,"work_id":"05745fc4-da64-4062-8c78-b9c263d13bab","year":2020},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:3e4e1242ca9c718f9b6ab8e72264ff2d6294614d83465ab4c5f7dee34ad5bf87","observation_id":"bcf00318-c2ae-4304-9370-d68c4dac14e4","resolution":{"observed_at":"2026-05-24T06:24:00.592476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d534a7fc-23a3-4a41-9f2c-f899199ffd8f","year":2008},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:95bd1fe38aa1ca793680fda08773642ffb4546fe2a9c5c2137c33323aabf4b3a","observation_id":"8d9c9f7e-0e55-4ffa-b3a9-ac43bd9a016a","resolution":{"observed_at":"2026-05-24T06:24:00.588966Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Moser and Gábor Tardos","venue":null,"work_id":"559d1afa-f5f5-4cf5-a1c5-686b28ead2cb","year":2009},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:bf600caddb4521ed4f08ad620d70a4a207ecd20cb0110f62c4ba8e7689739511","observation_id":"04920364-71f2-4d73-becd-9e43a440524e","resolution":{"observed_at":"2026-05-24T06:24:00.646340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Harris and Aravind Srinivasan","venue":null,"work_id":"1eb7a785-9dcd-45bf-a7ce-0bf2b8aa2a56","year":2017},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:20ac7762bdc9d9b79adc53ba9b73a992aac9ff393d18cada8e811a3c14281b4b","observation_id":"510202b6-c381-4bb3-940c-d5b582eb7d2c","resolution":{"observed_at":"2026-05-24T06:24:00.684732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Beyond the lovasz local lemma: Point to set correlations and their algorithmic applications","venue":null,"work_id":"5dc1b1f6-905b-4274-aa11-6d6c65856dcd","year":2020},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:24fcb6f662226ea4685d2a347e921cee2afd519265118d828569b953735a4307","observation_id":"99c913c4-4e4d-4cb9-ab53-c30d19764910","resolution":{"observed_at":"2026-05-24T06:24:00.693655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu","venue":null,"work_id":"b07b10c8-e9d8-4c74-b29f-ed031e9adc5a","year":2016},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:6ae2f460213182ccace81e4bbb57a19c27b76eb1eead986e751c8a296a83936c","observation_id":"1d6b3f79-db29-4d15-911a-be6636a6d1ff","resolution":{"observed_at":"2026-05-24T06:24:00.672393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Solving mixed integer programs using neural networks","venue":null,"work_id":"d53843ef-8d7f-4b25-87c4-06c6c6c30bbd","year":2020},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:8885d6cd2c147ce39def5157cc09719a8764e2a5afc1a4a4950ee25f331d5a9d","observation_id":"643e77a2-8d4d-414c-956f-a2391594e220","resolution":{"observed_at":"2026-05-24T06:24:00.667998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Erd˝os and L","venue":null,"work_id":"c94389c0-3c97-4c87-b7a4-4d22407854e3","year":1975},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:f9cc8dedcce618278a1cb94c3e0eb109e9d824aa8fd7031e40f79cb8709ac923","observation_id":"dfcc615c-0259-4825-8fbc-e77884b3ec3c","resolution":{"observed_at":"2026-05-24T06:24:00.619662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An algorithmic proof of the lovasz local lemma via resampling oracles","venue":null,"work_id":"197dbed9-5767-4e18-b835-b5c8b47dfc7f","year":2015},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:1228abf9cf8c7c8cd83a95e867eefdc0d06c4aae15e4c5565dfc8c6d8c812af1","observation_id":"08e330e6-aa14-44f9-b1c3-0d2aede16826","resolution":{"observed_at":"2026-05-24T06:24:00.682334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Schöning","venue":null,"work_id":"214b8be5-f620-45ac-9d1e-4fac4fe2d0c9","year":1999},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:8b5df9f61a5e30a4536ab81af60b8482a80b1fda2cd94636e856854163978dbe","observation_id":"aefb0d4d-2e68-40f0-9667-b7358f15a35a","resolution":{"observed_at":"2026-05-24T06:24:00.690430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Papadimitriou","venue":null,"work_id":"bfbd85f8-61f3-4b45-a5a3-25050e09f941","year":1991},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:b8c4afda934e681c6381752744c482892d55d45eab3da1571ea97f5570ae5851","observation_id":"635c6139-9640-4261-9bf9-8591b11a1b8a","resolution":{"observed_at":"2026-05-24T06:24:00.622662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Selman, H","venue":null,"work_id":"2b0c1066-1e71-4915-9040-b9dab693c645","year":1996},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:5866a222c683f03da1121209d79b356618d48b520d418185d38293a86b2119b9","observation_id":"5d661d67-01d1-475f-8131-62ad065e095b","resolution":{"observed_at":"2026-05-24T06:24:00.612927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Choosing probability distributions for stochastic local search and the role of make versus break","venue":null,"work_id":"7b49a729-7ee6-4d25-9db6-4bf6dac09611","year":2012},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:f6fb8c99317308e147a67d57155dd759546d9484e820a020eda47c6a580cf5ff","observation_id":"2e59eb02-061b-4f45-a0c9-b821280e3c44","resolution":{"observed_at":"2026-05-24T06:24:00.636564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Battaglia, Jessica B","venue":null,"work_id":"bcedb8b1-d53d-4449-badf-ab9d16f15cff","year":2018},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:04e7d437d0a051f95c0d3d181dc75f553aa702e6465c1826be34c6b150ff5240","observation_id":"d37cb0cf-f81f-417f-9be2-2758b7442bc9","resolution":{"observed_at":"2026-05-24T06:24:00.639794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"fd869d38-76ea-4efe-8623-60ae904a621c","year":2016},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:7f355a2281c02f452d8be681913a40c3920bd6289c80bab48c169277558b7150","observation_id":"d2f4210c-6247-4904-bcec-0c20fc06baac","resolution":{"observed_at":"2026-05-24T06:24:00.605884Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.08375","last_updated":"2026-04-14T12:21:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-22T14:30:17Z","title":"Deep Learning using Rectified Linear Units (ReLU)","version":3},"cited_work":{"arxiv_id":"1803.08375","doi":"10.48550/arxiv.1803.08375","metadata_source":"pith","pith_arxiv_id":"1803.08375","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Deep Learning using Rectified Linear Units (ReLU)","venue":"cs.NE","work_id":"1348fc83-94e6-4a01-b6c2-0568c6def951","year":2018},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"cited_paper":"/paper/1803.08375","citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:af25215613975504010df5172cd92f720362691f021f849c7a4d697d7637bd51","observation_id":"3c9e9bcf-f715-4e39-827e-eb17cfeaa577","resolution":{"observed_at":"2026-05-24T06:23:59.887532Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T15:51:08.672514+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T15:51:08.672514+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"92535f7c-0afc-4adb-9992-b770b752e29f","year":2012},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:c1f439136bab09f2ab77c7fc6a636a5e4119efb9c2d95953b4571984c1eeda5d","observation_id":"47e24c60-4185-409e-a599-48d31bfe40c7","resolution":{"observed_at":"2026-05-24T06:24:00.609445Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The Moser- Tardos Resample algorithm: Where is the limit? (an experimental inquiry), pages 159–171","venue":null,"work_id":"86d73d93-1f72-4a3c-ac56-d703f725c52c","year":null},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:af8ddacbae5978033fb8104277805ff1caa805835716e4d652cfea1f91d3013d","observation_id":"2cf45f99-9a06-4423-a48b-b931fe14a995","resolution":{"observed_at":"2026-05-24T06:24:00.664780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Critical behavior in the satisfiability of random boolean expressions","venue":null,"work_id":"235906fa-6800-4968-a5d1-4a0572b4bb61","year":1994},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:02b9adcc28fb6056f212ab2bace350737eb03710109c6c9b1de377e279ae2d07","observation_id":"bb6b0bde-95e9-4ea1-9ea0-a505f94dd3aa","resolution":{"observed_at":"2026-05-24T06:24:00.643265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"MassimoLauria/cnfgen: CNFgen registered with Zenodo, November 2019","venue":null,"work_id":"1fad36bd-640f-40f7-9954-90597126c7e3","year":2019},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:75bbf785cb42331a136fb4680d13be45a177779c911acb79520af62f14e5528b","observation_id":"c0397118-47fd-461f-a080-5b8b7e929063","resolution":{"observed_at":"2026-05-24T06:24:00.629580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the glucose sat solver","venue":null,"work_id":"48948e49-d50f-4ed7-a122-3c6768e3e1b6","year":2018},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:4aea914d2088a6b2e690f09d1cfb6ce9ea8df1a201dfc8c3fc3ecf00bca80eb7","observation_id":"9b8b080d-d57a-453e-b0b1-5b7d270fd66f","resolution":{"observed_at":"2026-05-24T06:24:00.632949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"PySAT: A Python toolkit for prototyping with SAT oracles","venue":null,"work_id":"73bd7078-b15b-491b-a58d-6ece02db79bd","year":2018},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:d70df56ae73f525b8d0eefdcbadfabf716a368fa555c940bb62c93e4c60e8ff4","observation_id":"8727c219-e0e0-4262-971e-b51106d3fb55","resolution":{"observed_at":"2026-05-24T06:24:00.626100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Satenstein: Automatically building local search sat solvers from components","venue":null,"work_id":"ebca6aad-ffb0-43cb-b314-85caf8125bab","year":2009},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:b7a8f31081bb57ad6f71ab41a362dcb5295932a00a77f9b918a937190a4d192a","observation_id":"278828f4-b829-4442-9d18-e540e8553efe","resolution":{"observed_at":"2026-05-24T06:24:00.602764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"JAX: composable transforma- tions of Python+NumPy programs","venue":null,"work_id":"eeb43c6f-ee89-42cf-87a0-5c80a7145269","year":2018},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:5186247bae56354ce797416b80e45b27b7953756cf1673d40ed254e1f41b8779","observation_id":"9c78b5bb-6a1b-4b37-9713-5765f7cb3318","resolution":{"observed_at":"2026-05-24T06:24:00.596046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Jraph: A library for graph neural networks in jax","venue":null,"work_id":"3ff1a150-7bc7-4d0f-bcb2-11fdaa837e5b","year":2020},"citing_paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-24T06:23:10.906146Z"},"links":{"citing_paper":"/paper/2309.11452"},"observation_digest":"sha256:2082c63a065658c06e597ab1b6f239f2a596c2f7fedc7d3f29912cc179caeb64","observation_id":"540628af-15a5-4f44-a4f4-f47cb7b60778","resolution":{"observed_at":"2026-05-24T06:24:00.599266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2309.11452","last_updated":"2026-04-16T12:12:05Z","latest_version":3,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-02T11:10:24.844403Z","submitted_at":"2023-09-20T16:27:52Z","title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":35},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2309.11452."}