{"as_of":"2026-08-13T17:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:72ec7423e5d08d55f7697aa4941f304443009dda9f5bf33bfad1789e626f3036","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":"2026-08-13T06:32:02.005865+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":"2026-08-12T16:55:50.671820Z","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":"2026-08-12T16:55:50.935665Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.06587","last_updated":"2021-02-12T15:53:48Z","snapshot_observed_at":"2026-08-11T09:15:02.349621Z","submitted_at":"2021-02-12T15:53:48Z","title":"Disturbing Reinforcement Learning Agents with Corrupted Rewards","version":1},"cited_work":{"arxiv_id":"2102.06587","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.06587","snapshot_observed_at":"2026-08-12T16:55:50.935665Z","title":"Disturbing Reinforcement Learning Agents with Corrupted Rewards","venue":"cs.LG","work_id":"7e3d98e9-3727-45cd-b5b0-b9d694b4166c","year":2021},"citing_paper":{"arxiv_id":"2411.13116","last_updated":"2024-11-20T08:20:29Z","snapshot_observed_at":"2026-08-13T02:16:58.884942Z","submitted_at":"2024-11-20T08:20:29Z","title":"Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T16:55:50.671820Z"},"links":{"cited_paper":"/paper/2102.06587","citing_paper":"/paper/2411.13116"},"observation_digest":"sha256:91e104d3613192d93e813d0b76208ac5d985fd8cc9920b63d66056ddfc33749d","observation_id":"60ec42e5-d81b-47b2-9098-175a027d6158","resolution":{"observed_at":"2026-08-12T16:55:50.941231Z","resolver_source":"local_arxiv","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/2102.06587/citation-record","integrity":"/paper/2102.06587/integrity","json":"/paper/2102.06587/citation-record.json","paper":"/paper/2102.06587"},"outbound":[],"paper":{"arxiv_id":"2102.06587","last_updated":"2021-02-12T15:53:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T09:15:02.349621Z","submitted_at":"2021-02-12T15:53:48Z","title":"Disturbing Reinforcement Learning Agents with Corrupted Rewards"},"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 1 inbound Pith citation observation for arXiv:2102.06587."}