{"as_of":"2026-08-10T20:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4271b7143e0b5bf57d43c45c80af5c21262ef5fb3358879dba3ce5187ad5dcd2","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":"2026-08-01T23:25:55.420922Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.06009","last_updated":"2021-06-10T19:06:28Z","snapshot_observed_at":"2026-08-10T19:34:11.064652Z","submitted_at":"2021-06-10T19:06:28Z","title":"Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06009","snapshot_observed_at":"2026-08-01T23:25:55.420922Z","title":"Jonker, and Ann Nowé","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-10T19:24:18.438719Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.420922Z"},"links":{"cited_paper":"/paper/2106.06009","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:4ffb60449f0f3b69f19dcf04ff44a58d9372426a923b9ddcd58ec6e839bec086","observation_id":"b47f40c2-c64f-43e2-ba65-233b89229d60","resolution":{"observed_at":"2026-08-01T23:25:55.420922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.06009/citation-record","integrity":"/paper/2106.06009/integrity","json":"/paper/2106.06009/citation-record.json","paper":"/paper/2106.06009"},"outbound":[],"paper":{"arxiv_id":"2106.06009","last_updated":"2021-06-10T19:06:28Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-10T19:34:11.064652Z","submitted_at":"2021-06-10T19:06:28Z","title":"Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule 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":"2026-08-10T06:31:04.303077+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:2106.06009."}