{"as_of":"2026-08-22T06:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:42c4db9bc6677a0f1b018cdef3bf8d4f06c9c379061e586aaa00d5e217aab3d0","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:55:14.897773Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T14:39:57.166065Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-08-16T00:55:14.897773Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02688","last_updated":"2025-06-16T12:26:27Z","snapshot_observed_at":"2026-08-19T19:54:20.762074Z","submitted_at":"2025-05-05T14:35:44Z","title":"Batch Sample-wise Stochastic Optimal Control via Stochastic Maximum Principle","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T00:55:14.897773Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2505.02688"},"observation_digest":"sha256:434746e286f9b2ec6eada2454dec1aa9f92caad86a9ec3d1013f4ceb8a85eab9","observation_id":"bf9a6a93-9dd2-4200-8670-06f8f31c200d","resolution":{"observed_at":"2026-08-16T00:55:14.897773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-08-06T13:23:15.328868Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.20898","last_updated":"2025-07-28T14:51:35Z","snapshot_observed_at":"2026-08-18T07:54:23.077251Z","submitted_at":"2025-07-28T14:51:35Z","title":"Iterative Schemes for Markov Perfect Equilibria","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T13:23:15.328868Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2507.20898"},"observation_digest":"sha256:ee41e35f9d92a311ef4c1ddf1f11baa60bcd47abd3a365cec7f44f4ffbf9f27b","observation_id":"15148c01-33eb-4266-90d1-8dca8abda665","resolution":{"observed_at":"2026-08-06T13:23:15.328868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":"2503.17869","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-07-04T14:39:57.166065Z","title":"Mete Soner, Josef Teichmann, and Qinxin Yan","venue":null,"work_id":"8b44504e-8f48-4399-bffa-47195567fe12","year":2025},"citing_paper":{"arxiv_id":"2604.06675","last_updated":"2026-04-08T04:48:25Z","snapshot_observed_at":"2026-08-16T20:43:34.703648Z","submitted_at":"2026-04-08T04:48:25Z","title":"An Effective Particle Gradient Projection Method for Solving Stochastic and Mean Field Control Problem","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T18:18:20.121201Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2604.06675"},"observation_digest":"sha256:ab06b828def7f1a5b2b68c90ffafade56335b26e5804cb6916962ec367070440","observation_id":"165c48e7-c6ec-41dd-ab41-e50f19d69ecc","resolution":{"observed_at":"2026-05-11T00:50:50.423902Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":"2503.17869","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-07-04T14:39:57.166065Z","title":"Mete Soner, Josef Teichmann, and Qinxin Yan","venue":null,"work_id":"8b44504e-8f48-4399-bffa-47195567fe12","year":2025},"citing_paper":{"arxiv_id":"2605.20718","last_updated":"2026-05-20T05:19:53Z","snapshot_observed_at":"2026-08-18T17:00:32.366322Z","submitted_at":"2026-05-20T05:19:53Z","title":"Policy Gradient for Continuous-Time Mean-Field Control","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-21T04:15:32.156380Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2605.20718"},"observation_digest":"sha256:5b39ee75785cad6b9bbee21b84136b48dc3ac20da899a135411da67f34987f11","observation_id":"288cf3e9-8ef0-43b6-bcb8-7d44fc1e9bb3","resolution":{"observed_at":"2026-05-21T04:19:33.514765Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":"2503.17869","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-07-04T14:39:57.166065Z","title":"Mete Soner, Josef Teichmann, and Qinxin Yan","venue":null,"work_id":"8b44504e-8f48-4399-bffa-47195567fe12","year":2025},"citing_paper":{"arxiv_id":"2606.26498","last_updated":"2026-06-25T01:05:27Z","snapshot_observed_at":"2026-08-06T05:47:59.384663Z","submitted_at":"2026-06-25T01:05:27Z","title":"Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T04:28:37.809929Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2606.26498"},"observation_digest":"sha256:64b9a1c9f1c6823b9c3ec20ebf400be4232e9f6b5c15898509e55aaa9e9c1614","observation_id":"30df1c83-9540-4414-830a-7d3ed037a806","resolution":{"observed_at":"2026-07-04T14:09:53.358583Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":"2503.17869","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-07-04T14:39:57.166065Z","title":"Mete Soner, Josef Teichmann, and Qinxin Yan","venue":null,"work_id":"8b44504e-8f48-4399-bffa-47195567fe12","year":2025},"citing_paper":{"arxiv_id":"2606.27181","last_updated":"2026-06-25T15:46:27Z","snapshot_observed_at":"2026-08-19T22:57:29.786840Z","submitted_at":"2026-06-25T15:46:27Z","title":"Numerical Approximation for Path-Dependent McKean-Vlasov Control with Non-Asymptotic Error Estimates","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-06-26T03:22:59.891609Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2606.27181"},"observation_digest":"sha256:0d53d31d12f0c6d4226793cfcd443aded9507395813dcbe4dc187a5e3d1569d5","observation_id":"688a4f95-ad2e-4742-9585-0ddc0fe43766","resolution":{"observed_at":"2026-07-04T14:39:57.167600Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-07-14T13:09:37.388826Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10254","last_updated":"2026-07-11T10:45:22Z","snapshot_observed_at":"2026-08-16T20:43:37.346793Z","submitted_at":"2026-07-11T10:45:22Z","title":"Neural feedback approximation for stochastic control with degenerate diffusions: error estimates and numerical analysis","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T13:09:37.388826Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2607.10254"},"observation_digest":"sha256:d769b2ecfdebc646cde59980f51cd9e0d09536a5f34d3dc24b8974c80fd7c706","observation_id":"fecc4d75-3157-47d5-a0a7-5012f64754fb","resolution":{"observed_at":"2026-07-14T13:09:37.388826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.17869","snapshot_observed_at":"2026-07-14T07:42:38.840826Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11005","last_updated":"2026-07-13T02:10:07Z","snapshot_observed_at":"2026-08-15T03:54:21.250968Z","submitted_at":"2026-07-13T02:10:07Z","title":"Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T07:42:38.840826Z"},"links":{"cited_paper":"/paper/2503.17869","citing_paper":"/paper/2607.11005"},"observation_digest":"sha256:79433398a84f9973fa1b2768ffc260d42315ad69b07fd7d71f111ffa594ea328","observation_id":"51b1dfa0-d23b-44aa-b097-0f5fa31eaab2","resolution":{"observed_at":"2026-07-14T07:42:38.840826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.17869/citation-record","integrity":"/paper/2503.17869/integrity","json":"/paper/2503.17869/citation-record.json","paper":"/paper/2503.17869"},"outbound":[],"paper":{"arxiv_id":"2503.17869","last_updated":"2025-03-22T22:07:59Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-20T09:27:24.683605Z","submitted_at":"2025-03-22T22:07:59Z","title":"Learning algorithms for mean field optimal control"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2503.17869."}