{"as_of":"2026-08-07T19:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:27203c5f2453a223f84d0a14260a0798d107a98a260247c86080153d473dddb9","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T19:47:32.487428Z","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":"2026-08-06T19:47:32.669219Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.00762","last_updated":"2022-07-28T17:30:20Z","snapshot_observed_at":"2026-07-06T12:24:25.416929Z","submitted_at":"2022-01-03T17:09:32Z","title":"Execute Order 66: Targeted Data Poisoning for Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2201.00762","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.00762","snapshot_observed_at":"2026-08-06T19:47:32.669219Z","title":"Execute Order 66: Targeted Data Poisoning for Reinforcement Learning","venue":"cs.LG","work_id":"2d7f1ed2-d3e7-4f73-869a-c1ef94c0db61","year":2022},"citing_paper":{"arxiv_id":"2507.04883","last_updated":"2025-07-07T11:15:54Z","snapshot_observed_at":"2026-08-06T19:34:47.468372Z","submitted_at":"2025-07-07T11:15:54Z","title":"Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T19:47:32.487428Z"},"links":{"cited_paper":"/paper/2201.00762","citing_paper":"/paper/2507.04883"},"observation_digest":"sha256:46a340a43c262319db0159ab179aed942b7fda95064ebf2be140970645ca0593","observation_id":"23081c6e-d028-4110-a649-2b0d995ee678","resolution":{"observed_at":"2026-08-06T19:47:32.674104Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2201.00762/citation-record","integrity":"/paper/2201.00762/integrity","json":"/paper/2201.00762/citation-record.json","paper":"/paper/2201.00762"},"outbound":[],"paper":{"arxiv_id":"2201.00762","last_updated":"2022-07-28T17:30:20Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:24:25.416929Z","submitted_at":"2022-01-03T17:09:32Z","title":"Execute Order 66: Targeted Data Poisoning for 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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2201.00762."}