{"as_of":"2026-08-09T17:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3eec2c2bdf54ae220f4fbc9893c78d1b5d5e11c4dd361a46bd72860203873ab2","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":"2026-08-09T06:31:02.800959+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":"2026-08-05T18:26:15.878908Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":13,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1712.00378","last_updated":"2022-01-27T10:10:49Z","snapshot_observed_at":"2026-08-02T20:12:43.470794Z","submitted_at":"2017-12-01T15:52:00Z","title":"Time Limits in Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00378","snapshot_observed_at":"2026-08-05T18:26:15.878908Z","title":"Available from: https://arxiv.org/abs/1712.00378","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.14761","last_updated":"2025-08-20T15:05:38Z","snapshot_observed_at":"2026-08-08T23:31:08.003105Z","submitted_at":"2025-08-20T15:05:38Z","title":"Reinforcement learning entangling operations on spin qubits","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T18:26:15.878908Z"},"links":{"cited_paper":"/paper/1712.00378","citing_paper":"/paper/2508.14761"},"observation_digest":"sha256:041b8be981d37a8b559b3476c5c37f6ecfb7814fbb22116faea1a39751373264","observation_id":"d87fe5f7-90c9-4861-a097-fca5d86cef22","resolution":{"observed_at":"2026-08-05T18:26:15.878908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00378","last_updated":"2022-01-27T10:10:49Z","snapshot_observed_at":"2026-08-02T20:12:43.470794Z","submitted_at":"2017-12-01T15:52:00Z","title":"Time Limits in Reinforcement Learning","version":4},"cited_work":{"arxiv_id":"1712.00378","doi":"10.48550/arxiv.1712.00378","metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00378","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Time limits in reinforcement learning","venue":"arXiv (Cornell University)","work_id":"06402836-77f0-4001-99eb-9513e141806f","year":2022},"citing_paper":{"arxiv_id":"2606.04574","last_updated":"2026-06-25T11:19:06Z","snapshot_observed_at":"2026-08-02T17:59:40.124402Z","submitted_at":"2026-06-03T08:10:33Z","title":"Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T07:38:03.222413Z"},"links":{"cited_paper":"/paper/1712.00378","citing_paper":"/paper/2606.04574"},"observation_digest":"sha256:e45d14d9f5b0e45b7ec2a88c8f834cd908c0c600c365c7a20a6203dda8875597","observation_id":"6acaebe9-c05c-4299-911f-8541ff47cfc7","resolution":{"observed_at":"2026-06-28T07:41:45.371681Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1712.00378/citation-record","integrity":"/paper/1712.00378/integrity","json":"/paper/1712.00378/citation-record.json","paper":"/paper/1712.00378"},"outbound":[],"paper":{"arxiv_id":"1712.00378","last_updated":"2022-01-27T10:10:49Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T20:12:43.470794Z","submitted_at":"2017-12-01T15:52:00Z","title":"Time Limits in 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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1712.00378."}