{"as_of":"2026-08-13T14:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a7f014532fbd57b8e5103f8d03e1be95fe2137d6dc8051c899a77de80a95ae2a","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:46:47.756731Z","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":"2026-07-03T04:17:36.728990Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.09304","last_updated":"2023-05-16T09:22:14Z","snapshot_observed_at":"2026-08-13T11:41:50.164846Z","submitted_at":"2023-05-16T09:22:14Z","title":"OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.09304","snapshot_observed_at":"2026-08-11T21:46:47.756731Z","title":"Omnisafe: An infrastructure for accelerating safe reinforcement learning research","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04153","last_updated":"2025-04-18T13:37:59Z","snapshot_observed_at":"2026-08-13T08:39:23.226153Z","submitted_at":"2024-12-05T13:32:02Z","title":"A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T21:46:47.756731Z"},"links":{"cited_paper":"/paper/2305.09304","citing_paper":"/paper/2412.04153"},"observation_digest":"sha256:10bdbe0edc698ea23e0e4da797cfbefe00d3b2891dd39183c45e90b5c20ac20e","observation_id":"cb1ef198-49d5-49f5-b475-be205d3957ff","resolution":{"observed_at":"2026-08-11T21:46:47.756731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.09304","last_updated":"2023-05-16T09:22:14Z","snapshot_observed_at":"2026-08-13T11:41:50.164846Z","submitted_at":"2023-05-16T09:22:14Z","title":"OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.09304","snapshot_observed_at":"2026-08-11T15:20:56.353163Z","title":"Omnisafe: An infrastructure for accelerating safe reinforcement learning research","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11138","last_updated":"2024-12-15T10:05:23Z","snapshot_observed_at":"2026-08-11T15:13:55.109460Z","submitted_at":"2024-12-15T10:05:23Z","title":"Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T15:20:56.353163Z"},"links":{"cited_paper":"/paper/2305.09304","citing_paper":"/paper/2412.11138"},"observation_digest":"sha256:529f2a08750f97242f49ec7c195bdb92e054a215fb0e9cd650de1031e1f29c10","observation_id":"b1796f7d-c120-452b-b1a1-62d0045df476","resolution":{"observed_at":"2026-08-11T15:20:56.353163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.09304","last_updated":"2023-05-16T09:22:14Z","snapshot_observed_at":"2026-08-13T11:41:50.164846Z","submitted_at":"2023-05-16T09:22:14Z","title":"OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.09304","snapshot_observed_at":"2026-08-11T11:30:06.685475Z","title":"arXiv preprint arXiv:2305.09304","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15429","last_updated":"2025-04-19T01:50:15Z","snapshot_observed_at":"2026-08-13T13:14:29.657716Z","submitted_at":"2024-12-19T22:29:03Z","title":"Offline Safe Reinforcement Learning Using Trajectory Classification","version":5},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T11:30:06.685475Z"},"links":{"cited_paper":"/paper/2305.09304","citing_paper":"/paper/2412.15429"},"observation_digest":"sha256:fe9d1e1dfcbfafdb468e56dd97ab13b190a03f24b7e5379b9cb47176a0eff9bd","observation_id":"9ee70c93-8be7-428c-8750-68b977ed3737","resolution":{"observed_at":"2026-08-11T11:30:06.685475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.09304","last_updated":"2023-05-16T09:22:14Z","snapshot_observed_at":"2026-08-13T11:41:50.164846Z","submitted_at":"2023-05-16T09:22:14Z","title":"OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research","version":1},"cited_work":{"arxiv_id":"2305.09304","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.09304","snapshot_observed_at":"2026-07-03T04:17:36.728990Z","title":null,"venue":null,"work_id":"b919ea4f-1dea-440a-bf55-c5054076d3e6","year":2023},"citing_paper":{"arxiv_id":"2606.11266","last_updated":"2026-06-09T04:46:37Z","snapshot_observed_at":"2026-08-07T14:06:41.140030Z","submitted_at":"2026-06-09T04:46:37Z","title":"Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T14:05:48.011688Z"},"links":{"cited_paper":"/paper/2305.09304","citing_paper":"/paper/2606.11266"},"observation_digest":"sha256:5c8342291a8556f5b75778994250f044e9dd873fa151af03faddf7524000e188","observation_id":"2774e6c0-5113-4270-bdff-308e8ed3925f","resolution":{"observed_at":"2026-07-03T04:17:36.730419Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.09304/citation-record","integrity":"/paper/2305.09304/integrity","json":"/paper/2305.09304/citation-record.json","paper":"/paper/2305.09304"},"outbound":[],"paper":{"arxiv_id":"2305.09304","last_updated":"2023-05-16T09:22:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T11:41:50.164846Z","submitted_at":"2023-05-16T09:22:14Z","title":"OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.09304."}