{"as_of":"2026-08-10T14:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f12b5c5ddd03bde934a57dddfa5898d937bf0a0a6e23fb04ce3afa0430fb8025","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":"2026-08-10T06:31:04.303077+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":"2026-08-07T00:32:54.102945Z","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":"2026-08-07T00:32:55.022274Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2301.12579","last_updated":"2023-07-03T04:36:44Z","snapshot_observed_at":"2026-07-06T14:45:52.127467Z","submitted_at":"2023-01-29T23:17:26Z","title":"Sample Efficient Deep Reinforcement Learning via Local Planning","version":2},"cited_work":{"arxiv_id":"2301.12579","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.12579","snapshot_observed_at":"2026-08-07T00:32:55.022274Z","title":"Sample Efficient Deep Reinforcement Learning via Local Planning","venue":"cs.LG","work_id":"14528dbb-37df-453f-93de-68bb5539129d","year":2023},"citing_paper":{"arxiv_id":"2506.13672","last_updated":"2025-06-16T16:30:00Z","snapshot_observed_at":"2026-08-09T12:19:47.488554Z","submitted_at":"2025-06-16T16:30:00Z","title":"The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T00:32:54.102945Z"},"links":{"cited_paper":"/paper/2301.12579","citing_paper":"/paper/2506.13672"},"observation_digest":"sha256:2b18d39412439084994bb87470f8edf685867788a40ad239c74bcfe93fd376b8","observation_id":"a72e8898-159d-41b9-a619-b35c1687e2dc","resolution":{"observed_at":"2026-08-07T00:32:55.136783Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2301.12579/citation-record","integrity":"/paper/2301.12579/integrity","json":"/paper/2301.12579/citation-record.json","paper":"/paper/2301.12579"},"outbound":[],"paper":{"arxiv_id":"2301.12579","last_updated":"2023-07-03T04:36:44Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:45:52.127467Z","submitted_at":"2023-01-29T23:17:26Z","title":"Sample Efficient Deep Reinforcement Learning via Local Planning"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2301.12579."}