{"as_of":"2026-08-17T04:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9bc421a2abc7c7819c16d09d72a1376f4c6e212e8120410ca08a2fe01feaeddd","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:40:45.589096Z","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":"2026-05-10T13:40:27.192976Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.11700","last_updated":"2024-01-17T04:52:39Z","snapshot_observed_at":"2026-08-16T15:22:28.361972Z","submitted_at":"2023-06-20T17:27:31Z","title":"Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11700","snapshot_observed_at":"2026-08-11T12:40:45.589096Z","title":"Last-iterate convergent policy gradient primal-dual methods for constrained MDPs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-17T04:10:34.741077Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.589096Z"},"links":{"cited_paper":"/paper/2306.11700","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:72d5f48eaf983e3b1e00d55cd6e6d90ba72b3ca3cc3efd46b0655a365ec90e8a","observation_id":"e7dc5fbf-0b77-45e9-bc34-8fb93dbf9c2d","resolution":{"observed_at":"2026-08-11T12:40:45.589096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11700","last_updated":"2024-01-17T04:52:39Z","snapshot_observed_at":"2026-08-16T15:22:28.361972Z","submitted_at":"2023-06-20T17:27:31Z","title":"Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs","version":2},"cited_work":{"arxiv_id":"2306.11700","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11700","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Last-iterate convergent policy gradient primal-dual methods for constrained mdps","venue":null,"work_id":"f132f4eb-4889-4285-9906-75fb9d350ddd","year":2023},"citing_paper":{"arxiv_id":"2604.14243","last_updated":"2026-04-17T15:08:39Z","snapshot_observed_at":"2026-08-11T13:36:47.085999Z","submitted_at":"2026-04-15T04:53:29Z","title":"Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T13:36:59.355147Z"},"links":{"cited_paper":"/paper/2306.11700","citing_paper":"/paper/2604.14243"},"observation_digest":"sha256:b47e9c945f7e8942b21d4d88778463a4a19094e03e0e7132ccc349c5ddd33b46","observation_id":"dfc9da6f-50f7-4f59-9b1c-dfd2cf955bd6","resolution":{"observed_at":"2026-05-10T13:40:27.195127Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.11700/citation-record","integrity":"/paper/2306.11700/integrity","json":"/paper/2306.11700/citation-record.json","paper":"/paper/2306.11700"},"outbound":[],"paper":{"arxiv_id":"2306.11700","last_updated":"2024-01-17T04:52:39Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-08-16T15:22:28.361972Z","submitted_at":"2023-06-20T17:27:31Z","title":"Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.11700."}