{"as_of":"2026-08-12T16:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9f5145787704a35edd6e4e362442d287821629a180ce38bf5679da8d69e8ee93","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":"2026-08-12T06:34:41.77262+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":"2026-08-12T14:44:45.472618Z","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":"2026-08-10T21:42:17.220035Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.10142","last_updated":"2024-09-18T15:11:24Z","snapshot_observed_at":"2026-08-11T17:39:54.511918Z","submitted_at":"2022-03-18T19:48:19Z","title":"Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.10142","snapshot_observed_at":"2026-08-12T14:44:45.472618Z","title":"Infinite-horizon reach-avoid zero-sum games via deep reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.15036","last_updated":"2024-11-22T16:08:42Z","snapshot_observed_at":"2026-08-12T14:32:28.731588Z","submitted_at":"2024-11-22T16:08:42Z","title":"Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T14:44:45.472618Z"},"links":{"cited_paper":"/paper/2203.10142","citing_paper":"/paper/2411.15036"},"observation_digest":"sha256:86b2aed6cd4262f1a03c4b4df07f5ed1a46b379d284b554b6826979cfed892eb","observation_id":"fa6647ee-d9b5-4e74-8a23-87a5455b9c8a","resolution":{"observed_at":"2026-08-12T14:44:45.472618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.10142","last_updated":"2024-09-18T15:11:24Z","snapshot_observed_at":"2026-08-11T17:39:54.511918Z","submitted_at":"2022-03-18T19:48:19Z","title":"Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2203.10142","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.10142","snapshot_observed_at":"2026-08-10T21:42:17.220035Z","title":"Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning","venue":"eess.SY","work_id":"41e28fc8-7e71-4901-96c4-87b57def8bd2","year":2022},"citing_paper":{"arxiv_id":"2501.04276","last_updated":"2025-02-19T14:13:51Z","snapshot_observed_at":"2026-08-11T11:50:42.037774Z","submitted_at":"2025-01-08T04:54:28Z","title":"Bridging Adaptivity and Safety: Learning Agile Collision-Free Locomotion Across Varied Physics","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T21:42:16.362374Z"},"links":{"cited_paper":"/paper/2203.10142","citing_paper":"/paper/2501.04276"},"observation_digest":"sha256:873e71d8fce04bbc8f309f74c25907f4fc053e33850b1c130fe4972e9ade700c","observation_id":"99d49efc-d293-4d22-bcfd-f5d57f8d3d9b","resolution":{"observed_at":"2026-08-10T21:42:17.225127Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2203.10142/citation-record","integrity":"/paper/2203.10142/integrity","json":"/paper/2203.10142/citation-record.json","paper":"/paper/2203.10142"},"outbound":[],"paper":{"arxiv_id":"2203.10142","last_updated":"2024-09-18T15:11:24Z","latest_version":3,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-11T17:39:54.511918Z","submitted_at":"2022-03-18T19:48:19Z","title":"Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2203.10142."}