{"as_of":"2026-08-11T00:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2645055123e4ece41f11fb12c90bf87ddefb94f0474fb831639a18832d72177e","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":"2026-08-10T06:31:04.303077+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":"2026-05-20T09:42:42.767980Z","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":"2026-05-20T09:43:10.612297Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2208.05060","last_updated":"2022-08-09T22:20:41Z","snapshot_observed_at":"2026-07-06T13:40:27.644738Z","submitted_at":"2022-08-09T22:20:41Z","title":"Multi-Agent Learning for Resilient Distributed Control Systems","version":1},"cited_work":{"arxiv_id":"2208.05060","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2208.05060","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"9ecccfea-349c-405c-b427-a9e5a8cc24fd","year":2022},"citing_paper":{"arxiv_id":"2605.17886","last_updated":"2026-05-18T05:53:06Z","snapshot_observed_at":"2026-08-07T03:20:26.143683Z","submitted_at":"2026-05-18T05:53:06Z","title":"Cooperative and Noncooperative Paradigms for Game-Theoretic Control of Socio-Technical Systems","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-05-20T09:42:42.767980Z"},"links":{"cited_paper":"/paper/2208.05060","citing_paper":"/paper/2605.17886"},"observation_digest":"sha256:d15f1d54aea7f3d0b4ec80798c7f9fcc7a0bb5ed6a00c8ddf09a90235692cb54","observation_id":"a163e172-20f8-49ba-917d-a5672efa06e0","resolution":{"observed_at":"2026-05-20T09:43:10.613945Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2208.05060/citation-record","integrity":"/paper/2208.05060/integrity","json":"/paper/2208.05060/citation-record.json","paper":"/paper/2208.05060"},"outbound":[],"paper":{"arxiv_id":"2208.05060","last_updated":"2022-08-09T22:20:41Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-07-06T13:40:27.644738Z","submitted_at":"2022-08-09T22:20:41Z","title":"Multi-Agent Learning for Resilient Distributed Control Systems"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2208.05060."}