{"as_of":"2026-08-15T16:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2147df621b318aa0e4dc743b709fba1edd7eff560d98676419e3680c7e3e5e83","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":"2026-08-15T06:32:42.880941+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":"2026-08-11T05:58:49.234446Z","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":"2026-08-10T20:56:51.348417Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.03814","last_updated":"2024-09-02T05:18:49Z","snapshot_observed_at":"2026-08-13T05:08:31.715251Z","submitted_at":"2023-12-06T18:29:23Z","title":"Pearl: A Production-ready Reinforcement Learning Agent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03814","snapshot_observed_at":"2026-08-11T05:58:49.234446Z","title":"https://arxiv","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16970","last_updated":"2025-08-10T19:30:14Z","snapshot_observed_at":"2026-08-15T16:36:47.030051Z","submitted_at":"2024-12-22T11:02:13Z","title":"A Research Agenda for Usability and Generalisation in Reinforcement Learning","version":2},"reference_index":173,"source":"pdf_text","source_observed_at":"2026-08-11T05:58:49.234446Z"},"links":{"cited_paper":"/paper/2312.03814","citing_paper":"/paper/2412.16970"},"observation_digest":"sha256:05c0a062ce9f986770799b6f0a31b80eac20e8d828b575457a5e4e9adfc7dea3","observation_id":"a0362466-de50-49a1-996b-6e6a91669ed7","resolution":{"observed_at":"2026-08-11T05:58:49.234446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03814","last_updated":"2024-09-02T05:18:49Z","snapshot_observed_at":"2026-08-13T05:08:31.715251Z","submitted_at":"2023-12-06T18:29:23Z","title":"Pearl: A Production-ready Reinforcement Learning Agent","version":2},"cited_work":{"arxiv_id":"2312.03814","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.03814","snapshot_observed_at":"2026-08-10T20:56:51.348417Z","title":"Pearl: A Production-ready Reinforcement Learning Agent","venue":"cs.LG","work_id":"113d8411-736b-4e02-b2f3-c20e20f2380b","year":2023},"citing_paper":{"arxiv_id":"2501.06937","last_updated":"2025-01-12T21:24:27Z","snapshot_observed_at":"2026-08-15T14:32:10.018340Z","submitted_at":"2025-01-12T21:24:27Z","title":"An Empirical Study of Deep Reinforcement Learning in Continuing Tasks","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T20:56:51.137298Z"},"links":{"cited_paper":"/paper/2312.03814","citing_paper":"/paper/2501.06937"},"observation_digest":"sha256:422c0f7d5434bec92697aa4561de7455a2bb4e184567ff4cb2d41a28d3829d39","observation_id":"47f1ddd2-023b-4c39-8c16-9f6ae5e4777a","resolution":{"observed_at":"2026-08-10T20:56:51.352949Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2312.03814/citation-record","integrity":"/paper/2312.03814/integrity","json":"/paper/2312.03814/citation-record.json","paper":"/paper/2312.03814"},"outbound":[],"paper":{"arxiv_id":"2312.03814","last_updated":"2024-09-02T05:18:49Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T05:08:31.715251Z","submitted_at":"2023-12-06T18:29:23Z","title":"Pearl: A Production-ready Reinforcement Learning Agent"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.03814."}