{"as_of":"2026-08-10T05:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c69b4d78e54b8c778947ede1d88c124be445ae872cb45ef20584b9a5c150f608","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:10:13.774546Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T13:10:28.428600Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.13912","last_updated":"2021-05-31T17:08:26Z","snapshot_observed_at":"2026-07-06T09:32:26.221291Z","submitted_at":"2020-06-24T17:45:44Z","title":"Unified Reinforcement Q-Learning for Mean Field Game and Control Problems","version":3},"cited_work":{"arxiv_id":"2006.13912","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.13912","snapshot_observed_at":"2026-08-07T13:10:28.428600Z","title":"Unified Reinforcement Q-Learning for Mean Field Game and Control Problems","venue":"math.OC","work_id":"5c6c14af-37f6-41c5-8944-89b0fa6ef70f","year":2020},"citing_paper":{"arxiv_id":"2505.22781","last_updated":"2025-05-28T18:50:25Z","snapshot_observed_at":"2026-08-10T01:56:03.351457Z","submitted_at":"2025-05-28T18:50:25Z","title":"Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T13:10:13.774546Z"},"links":{"cited_paper":"/paper/2006.13912","citing_paper":"/paper/2505.22781"},"observation_digest":"sha256:b4e3e052a1ad5bb04b1ada0cce64e10987c96f883f950748077ce6277674c3cc","observation_id":"570de5b3-c6e6-4ed4-84d7-b2cd89a7bb11","resolution":{"observed_at":"2026-08-07T13:10:28.517786Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2006.13912/citation-record","integrity":"/paper/2006.13912/integrity","json":"/paper/2006.13912/citation-record.json","paper":"/paper/2006.13912"},"outbound":[],"paper":{"arxiv_id":"2006.13912","last_updated":"2021-05-31T17:08:26Z","latest_version":3,"primary_category":"math.OC","snapshot_observed_at":"2026-07-06T09:32:26.221291Z","submitted_at":"2020-06-24T17:45:44Z","title":"Unified Reinforcement Q-Learning for Mean Field Game and Control Problems"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2006.13912."}