{"as_of":"2026-08-18T17:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2954ed20549e86a5986aeec52e8525909f9d4a401a00f37bb1f57cdcca96efd4","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:11:01.165823Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2501.05845/citation-record","integrity":"/paper/2501.05845/integrity","json":"/paper/2501.05845/citation-record.json","paper":"/paper/2501.05845"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:02.454244Z","title":"Katzgraber","venue":null,"work_id":"dd5a92b2-5881-447e-a9fd-f03d13a2dc1c","year":2019},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.491030Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:3ffc926079606e806a3ef60dfd31a19764c3672690726cfbc7ed09d481ea6ee2","observation_id":"14480c31-0062-457a-a44f-b0e0ad0b043b","resolution":{"observed_at":"2026-08-10T21:11:02.461803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:00.496970Z","title":"Fast unfolding of communities in large networks","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.496970Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:6b589e2f4bdb09ff238555369865c0efde3b8a2eb3d2524363a78387faf90cb1","observation_id":"ecee3d28-5d60-43d8-a99e-096bb0201a69","resolution":{"observed_at":"2026-08-10T21:11:00.496970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:00.502359Z","title":"Approximating maximum independent sets by excluding subgraphs","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.502359Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:a498f04f91898980e6d2114b03e82a5f3be4b3ee93f24d84c1a0c0a5362b01d8","observation_id":"43648a96-14d2-447e-b17d-81478ab90fba","resolution":{"observed_at":"2026-08-10T21:11:00.502359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:00.507404Z","title":"How attentive are graph attention networks? In International Conference on Learning Representations, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.507404Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:f6439dd796c35f39c89b97b117520d8e8ee5152ddb9c3667a1ba6313c6aed46b","observation_id":"7050f240-6bfe-4696-894a-71d305e4b564","resolution":{"observed_at":"2026-08-10T21:11:00.507404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:02.379735Z","title":"Maximum independent set: Self-training through dynamic programming","venue":null,"work_id":"66a42677-8c7a-4aa7-ab0c-cdbccb3cfff7","year":2024},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.512763Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:18e5473947107324b2f20772f0f9956211f09416d72be1af0dda93a03805ebd7","observation_id":"0a4da819-c2f8-4c08-a8f8-4cf9a0d3b83e","resolution":{"observed_at":"2026-08-10T21:11:02.385884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.352533Z","title":"Finding Maximum Cliques on the D-Wave Quantum Annealer","venue":null,"work_id":"75b64850-8b55-4755-b607-122cc9ab09de","year":2019},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.518430Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:5cd4ae595fbc3352bf9d98fb73d473350abf0ab897b662b755e8e9bafaec4e5e","observation_id":"4df8a719-ce29-4cc3-99aa-9f83169c8233","resolution":{"observed_at":"2026-08-10T21:11:02.358987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.332879Z","title":"Clustering approach for solving traveling salesman problems via ising model based solver","venue":null,"work_id":"486c3a87-d1b5-4b4e-a564-98ef31bf71ff","year":2020},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.524197Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:8b4a1cec22700e9284e2a79ba960a3022cada04de8d7b9a3c82dd6b2820ec1cb","observation_id":"ccc36921-5efb-474f-b992-f157abd70f0c","resolution":{"observed_at":"2026-08-10T21:11:02.338815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.310105Z","title":"Qubo formulations for training machine learning models","venue":null,"work_id":"96aca657-a310-4560-b966-47f6fb842cb5","year":2021},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.539602Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:0468d8b48f855838dd6369658f7daf38d61d966a838daabfbdff75b07619c2d4","observation_id":"9c657449-3fd0-44d1-8ba9-727a1ea79d2d","resolution":{"observed_at":"2026-08-10T21:11:02.316025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.285382Z","title":"Graph communities in neo4j: Four algorithms at work","venue":null,"work_id":"b1097654-da61-4d6e-b133-64153901a3e9","year":2020},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.546210Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:3a88f738c87ae0e8066fdaf61fe0c2ee74021678bbe167bd5487f0a10b765b9f","observation_id":"9e6f9146-fcd1-4dc2-98ae-dcb75a6f37a6","resolution":{"observed_at":"2026-08-10T21:11:02.290304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.262254Z","title":"Quantum bridge analytics i: a tutorial on formulating and using qubo models.Annals of Operations Research, 314(1):141–183, 2022","venue":null,"work_id":"d7b5e557-0286-46bb-aa9e-08ab09d086c8","year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.554751Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:17da02fd6174487d131fbb61a09ba0b3201035eeb56e0db07ebb6a020293789e","observation_id":"1813a1b1-eece-46b7-b3f3-3e1709a57d29","resolution":{"observed_at":"2026-08-10T21:11:02.272000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.226373Z","title":"High-performance combinatorial optimization based on classical mechanics","venue":null,"work_id":"153b1b58-58da-4cf2-b49c-1acd57a46c1f","year":2021},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.565739Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:9f09c1fa39d8478ae57f5038dc6b2774966942c8a45134c01b27ccae89ee0590","observation_id":"9b642755-0801-4610-b1ad-e7131c25ddc8","resolution":{"observed_at":"2026-08-10T21:11:02.236492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.199892Z","title":null,"venue":null,"work_id":"8fe1793f-90bf-421c-ac0b-7ced29324bbd","year":2019},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.594754Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:b7dab49367bb49a1f6d54adaff807d3d733cf915eef55fe535c2ad78a2ea9a63","observation_id":"525a3726-d481-4c4d-b4cb-6a9c07de5df3","resolution":{"observed_at":"2026-08-10T21:11:02.208335Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:00.634754Z","title":"Exploring network structure, dynamics, and function using networkx","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.634754Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:e891605356ce591dfec22496ebbaf6afeb55ebe013268898a1a3174937c7d645","observation_id":"8ecbc48f-6ea9-4141-b53a-28afff48ddd8","resolution":{"observed_at":"2026-08-10T21:11:00.634754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:02.158656Z","title":null,"venue":null,"work_id":"0281a762-10c6-494d-9d02-e51daf041919","year":2019},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.674755Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:8d9bb299a8793e008c5453d51f9519587cc1ea302b681d077ac4af97308a11a3","observation_id":"a907e310-8208-4ea7-ba0f-eb5eaea274e5","resolution":{"observed_at":"2026-08-10T21:11:02.165464Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.097016Z","title":"Graph representation learning","venue":null,"work_id":"27a974a4-9c7f-4636-aabf-b248f84a53a3","year":2020},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.697814Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:bc88ce38848184511a3957f536edb149dd4aa0092d348e151c4c5ba14bef74ef","observation_id":"b5de63b3-60be-422b-b8cf-a7dbbad6a7e0","resolution":{"observed_at":"2026-08-10T21:11:02.123906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:02.049729Z","title":"Enhancing quantum annealing performance for the molecular similarity problem","venue":null,"work_id":"7bb6a0be-512c-471e-8d54-207317a16db4","year":2017},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.711224Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:59cb976f83af361440b7fe5b6aad79d8acc4f84fbde111919a03c5cfe571c31b","observation_id":"195cea2a-6b84-486f-b742-9ff812bd13e8","resolution":{"observed_at":"2026-08-10T21:11:02.071874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.998890Z","title":"100,000-spin coherent Ising machine","venue":null,"work_id":"69c5e868-f76b-4c9d-8529-5a286809a4b7","year":2021},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.724751Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:350d4f4ad085bef2b3aa08a0843107ab705cff2b643801d42cab3fbde2b4ef9b","observation_id":"fce35953-344c-43dd-b534-b67f58c24892","resolution":{"observed_at":"2026-08-10T21:11:02.015765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.964740Z","title":null,"venue":null,"work_id":"92b85ef9-8a3a-4c7c-94c1-b305b3367d80","year":2011},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.762951Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:75a4eaca0a8050e2a4535148a519b5f115e3d0a24209285ae594c0c59d8b48a2","observation_id":"61a21de9-d823-43ef-8dbb-79a9919519c1","resolution":{"observed_at":"2026-08-10T21:11:01.983015Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.892629Z","title":"Accelerating training and inference of graph neural networks with fast sampling and pipelining","venue":null,"work_id":"aaa2af8e-ce07-49dc-86f2-92b3f1062bfb","year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.793864Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:7c2c5fdaabf0ec3cf04563c5b1f3d3b199f58a58e55e3386dbb16aabb6270d87","observation_id":"fcd3c623-d54e-4fcf-8820-b774fdcb39d0","resolution":{"observed_at":"2026-08-10T21:11:01.915731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.856397Z","title":"McMahon, and Tim Byrnes","venue":null,"work_id":"a5792f2a-92bc-4c1e-8058-d5c78b616fab","year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.821033Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:f90b9a97202e094b80caca6f78bf9fdf87872facb86ff4e2dd67041640d96ee4","observation_id":"eea9c914-c457-4045-967c-4ac708eaf5a9","resolution":{"observed_at":"2026-08-10T21:11:01.864942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.809167Z","title":"Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks","venue":null,"work_id":"27a2c35c-1998-47c1-af04-ff37d6da7ee8","year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.854874Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:907ab6def2dad0d39c1e3ac30f21b9e237632dec2128573f2105b5be32659c4d","observation_id":"945b2399-2251-4820-a0fb-bc748dfef7ca","resolution":{"observed_at":"2026-08-10T21:11:01.823957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.750687Z","title":"Traffic flow optimization using a quantum annealer","venue":null,"work_id":"91a42c3f-8576-4892-9987-1a0781942b70","year":2017},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.895320Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:e64fdba60bcbbb83b4f9edd80eda7a19a9f10ac250b38fef6786062f3bd267f4","observation_id":"a1a4c9b8-4cf6-45a2-a6b2-bed35911793c","resolution":{"observed_at":"2026-08-10T21:11:01.765689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.710736Z","title":"Binary optimization by momentum annealing","venue":null,"work_id":"2f523ffc-d8d2-4a77-9fd9-3a2c9a313457","year":2019},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.920303Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:496e49f995f2c539bf4cccdba14947b04e42d2816c80aeaacb0c5764a33fa1fa","observation_id":"b43f3866-4e0b-4f8f-8227-906353cf7065","resolution":{"observed_at":"2026-08-10T21:11:01.724739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.676975Z","title":"Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer","venue":null,"work_id":"fe72bd6a-6a3e-4e80-b083-61d6855d1606","year":2016},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.932353Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:954c08660a9547f9d6334f7a0555846a74230de01f1ab5f6ed52c1f358a0b47a","observation_id":"1d8373eb-af15-4fa9-8f48-321fbdc18554","resolution":{"observed_at":"2026-08-10T21:11:01.690988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:00.947145Z","title":"Combinatorial optimization with physics-inspired graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.947145Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:48183eb3bc30f261166233412d82e66117ea09f16f95bc3f5218db90202157a1","observation_id":"abaed734-125e-4d6d-ab73-04304c9e1c57","resolution":{"observed_at":"2026-08-10T21:11:00.947145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:01.575749Z","title":"A Decomposition Method for Makespan Minimization in Job-Shop Scheduling Problem Using Ising Machine","venue":null,"work_id":"8f7e56c7-0884-4db1-a764-f37b8c7c968c","year":2021},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.973265Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:6594f9f1279da27d2a95e7228e64ebf59a28a264422ce0db5d49762a5b54d36b","observation_id":"869ab042-6604-4aed-a378-5228ab769ce3","resolution":{"observed_at":"2026-08-10T21:11:01.604745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.549298Z","title":"Graph neural networks for maximum constraint satisfaction","venue":null,"work_id":"42aee812-7030-4a9b-91de-bc655a5650df","year":2021},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:00.994793Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:6dc47b9f470bc8b3cf1fb1db0db44bebbb5d4d53a14bc02410894339c6bc591e","observation_id":"f883ab4f-cf1c-416a-b37d-f6c12761313a","resolution":{"observed_at":"2026-08-10T21:11:01.558731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.511541Z","title":"An accelerator architecture for combinatorial optimization problems","venue":null,"work_id":"1462f1ea-7dbf-4b6a-a10c-5e6c59cba726","year":2017},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:01.000993Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:1a3e64e73ea2fe557f51c6937082c186e79769a183899073bd2b6b22aa4601b1","observation_id":"f4a59930-69d4-428f-bd0d-1ff16e2619af","resolution":{"observed_at":"2026-08-10T21:11:01.516977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:01.029034Z","title":"Graph attention networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:01.029034Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:fd97b94bcd5d788ad5c3a88fc739a372d1c227564a0cb2e24de6932a102448c5","observation_id":"9300e9a0-f4d5-4a8c-af20-102d8948fb16","resolution":{"observed_at":"2026-08-10T21:11:01.029034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:11:01.444766Z","title":"A 1.3-Mbit Annealing System Composed of Fully-Synchronized 9-board x 9-chip x 16-kbit Annealing Processor Chips for Large-Scale Combinatorial Optimization Problems","venue":null,"work_id":"a6fbb0da-b156-46a8-bae1-c000e69a9625","year":2021},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:01.070057Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:099f7e63cf914784a53c94dafe42bc7c8cb9d96668f2243ee50fef3fe97064bb","observation_id":"dd2306f1-e5b3-4131-9377-2872aa4d907f","resolution":{"observed_at":"2026-08-10T21:11:01.450485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.335282Z","title":"20k-spin Ising chip for combinational optimization problem with CMOS annealing","venue":null,"work_id":"7d068bdd-75b6-412a-a8af-ad8a2c184080","year":2015},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:01.125830Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:308417f87f26d47705800412d7157053d37560ffa17fde6ec5887e04793d6a05","observation_id":"22a7a062-0110-448d-a33a-cd4fb07c410c","resolution":{"observed_at":"2026-08-10T21:11:01.361704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.297330Z","title":"QUBO-inspired Molecular Fingerprint for Chemical Property Prediction","venue":null,"work_id":"782add98-ca9a-4901-8b71-6de644a1547c","year":2022},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:01.153126Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:28b4520a3ba38f73f1d69518f07ea592ad719eee43d6885c6bfe97c0f039d3f6","observation_id":"6002b6b3-8a6e-4a33-a972-759e0947ff3c","resolution":{"observed_at":"2026-08-10T21:11:01.305491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T21:11:01.270404Z","title":"Distdgl: distributed graph neural network training for billion-scale graphs","venue":null,"work_id":"dc5e2194-46cf-4200-aa31-0ec7e5251a93","year":2020},"citing_paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:11:01.165823Z"},"links":{"citing_paper":"/paper/2501.05845"},"observation_digest":"sha256:9e16a3ff9bea05586b8fc504624b7d56174e35551b1a91ddc0511c740c3e5bb3","observation_id":"53a9395c-5e26-4d38-b5fe-8eb427c20164","resolution":{"observed_at":"2026-08-10T21:11:01.279628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.05845","last_updated":"2025-01-10T10:36:46Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-17T01:29:16.298609Z","submitted_at":"2025-01-10T10:36:46Z","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":24},"total_outbound_references":33},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2501.05845."}