{"as_of":"2026-08-15T05:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e0ce5e463c8c2620fd8f5a4c0ec3a615d3cbc26116c5cc6298f396ddb6c6b85e","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":"2026-08-14T06:32:32.682623+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":"2026-08-11T13:32:45.157572Z","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":"2026-08-11T13:32:45.642944Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2301.12950","last_updated":"2023-05-31T09:08:07Z","snapshot_observed_at":"2026-08-14T02:12:32.650206Z","submitted_at":"2023-01-30T14:50:46Z","title":"Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs","version":2},"cited_work":{"arxiv_id":"2301.12950","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.12950","snapshot_observed_at":"2026-08-11T13:32:45.642944Z","title":"Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs","venue":"cs.LG","work_id":"5ce8bb6c-d1e7-41d2-a8be-94b9ad0b47c0","year":2023},"citing_paper":{"arxiv_id":"2412.13053","last_updated":"2024-12-17T16:15:04Z","snapshot_observed_at":"2026-08-12T04:27:13.118081Z","submitted_at":"2024-12-17T16:15:04Z","title":"SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T13:32:45.157572Z"},"links":{"cited_paper":"/paper/2301.12950","citing_paper":"/paper/2412.13053"},"observation_digest":"sha256:aebd9e886ff550189fdcfdef1281d8e2f5fe8148d720a23326d8cc6605569abc","observation_id":"ef7ad309-a82c-4406-9a4f-b630eb0f41dc","resolution":{"observed_at":"2026-08-11T13:32:45.648688Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2301.12950/citation-record","integrity":"/paper/2301.12950/integrity","json":"/paper/2301.12950/citation-record.json","paper":"/paper/2301.12950"},"outbound":[],"paper":{"arxiv_id":"2301.12950","last_updated":"2023-05-31T09:08:07Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T02:12:32.650206Z","submitted_at":"2023-01-30T14:50:46Z","title":"Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2301.12950."}