{"as_of":"2026-08-16T16:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eea92dacaf0469ada3275f3686c377c87c875b287f4c8a21c5e1676f59d26ccc","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":"2026-08-15T14:54:45.758379Z","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":"2026-05-15T02:53:33.829015Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2202.07565","last_updated":"2022-02-15T16:49:28Z","snapshot_observed_at":"2026-08-13T16:40:18.196145Z","submitted_at":"2022-02-15T16:49:28Z","title":"CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2202.07565","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.07565","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cup: A conservative update policy algorithm for safe reinforcement learning","venue":null,"work_id":"a9d97fc6-85b8-4fe3-8890-e6a733313cd1","year":2022},"citing_paper":{"arxiv_id":"2605.14246","last_updated":"2026-05-14T01:23:09Z","snapshot_observed_at":"2026-08-13T19:08:08.660958Z","submitted_at":"2026-05-14T01:23:09Z","title":"Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-15T02:50:40.285132Z"},"links":{"cited_paper":"/paper/2202.07565","citing_paper":"/paper/2605.14246"},"observation_digest":"sha256:3a1822ae5f6dc514ddfa0019e00c6d442ed50751c4a7c638ed6a09d5ebe4d973","observation_id":"2402e5d0-0922-48c4-84fb-38a471426a2a","resolution":{"observed_at":"2026-05-15T02:53:33.830811Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07565","last_updated":"2022-02-15T16:49:28Z","snapshot_observed_at":"2026-08-13T16:40:18.196145Z","submitted_at":"2022-02-15T16:49:28Z","title":"CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.07565","snapshot_observed_at":"2026-08-15T14:54:45.758379Z","title":"Cup: A conservative update policy algorithm for safe reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.03409","last_updated":"2026-08-04T10:04:08Z","snapshot_observed_at":"2026-08-16T12:49:16.223400Z","submitted_at":"2026-08-04T10:04:08Z","title":"Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T14:54:45.758379Z"},"links":{"cited_paper":"/paper/2202.07565","citing_paper":"/paper/2608.03409"},"observation_digest":"sha256:7ecf8f3bf29c54f1de12f02c8e18769092e3744acadc35faa29cf26a5072c96f","observation_id":"b972b589-e656-446d-807d-5067f6c8cf55","resolution":{"observed_at":"2026-08-15T14:54:45.758379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2202.07565/citation-record","integrity":"/paper/2202.07565/integrity","json":"/paper/2202.07565/citation-record.json","paper":"/paper/2202.07565"},"outbound":[],"paper":{"arxiv_id":"2202.07565","last_updated":"2022-02-15T16:49:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T16:40:18.196145Z","submitted_at":"2022-02-15T16:49:28Z","title":"CUP: A Conservative Update Policy Algorithm for Safe 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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.07565."}