{"as_of":"2026-08-09T11:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5ad2c14ab41c1a1488a3c23ebe26ad5c81498fc92e700e6d7259ac14ea0c517d","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":"2026-08-08T11:56:41.359434Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T12:06:55.518875Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.05498","last_updated":"2025-01-09T17:47:17Z","snapshot_observed_at":"2026-08-03T15:51:52.377308Z","submitted_at":"2025-01-09T17:47:17Z","title":"Generative Flow Networks: Theory and Applications to Structure Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.05498","snapshot_observed_at":"2026-08-08T11:56:41.359434Z","title":"Generative flow networks: Theory and applications to structure learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.07735","last_updated":"2025-09-11T14:32:19Z","snapshot_observed_at":"2026-08-08T11:42:32.276078Z","submitted_at":"2025-02-11T17:55:03Z","title":"Revisiting Non-Acyclic GFlowNets in Discrete Environments","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T11:56:41.359434Z"},"links":{"cited_paper":"/paper/2501.05498","citing_paper":"/paper/2502.07735"},"observation_digest":"sha256:a56cd9dd3c3fb339d82659243c0413f58a3c302115208d0f12d079c5a6d95881","observation_id":"2900ccd5-53d4-494c-b87e-2a36b2150404","resolution":{"observed_at":"2026-08-08T11:56:41.359434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.05498","last_updated":"2025-01-09T17:47:17Z","snapshot_observed_at":"2026-08-03T15:51:52.377308Z","submitted_at":"2025-01-09T17:47:17Z","title":"Generative Flow Networks: Theory and Applications to Structure Learning","version":1},"cited_work":{"arxiv_id":"2501.05498","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.05498","snapshot_observed_at":"2026-07-02T12:06:55.518875Z","title":"arXiv preprint arXiv:2501.05498 , year=","venue":null,"work_id":"f007c6ac-e629-4305-98c7-7a83aeab0524","year":null},"citing_paper":{"arxiv_id":"2606.06272","last_updated":"2026-06-04T15:14:24Z","snapshot_observed_at":"2026-08-05T16:40:32.420875Z","submitted_at":"2026-06-04T15:14:24Z","title":"Your GFlowNet Secretly Learns an Optimal Transport Plan","version":1},"reference_index":155,"source":"arxiv_source","source_observed_at":"2026-06-28T02:35:08.323804Z"},"links":{"cited_paper":"/paper/2501.05498","citing_paper":"/paper/2606.06272"},"observation_digest":"sha256:b8b95b526765ba6c0f45c5ad36a0c7a0115ceea920624221964c63eca0cbdad6","observation_id":"b64c894a-e72f-435d-908a-c9850a8a5c8c","resolution":{"observed_at":"2026-07-02T12:06:55.520243Z","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/2501.05498/citation-record","integrity":"/paper/2501.05498/integrity","json":"/paper/2501.05498/citation-record.json","paper":"/paper/2501.05498"},"outbound":[],"paper":{"arxiv_id":"2501.05498","last_updated":"2025-01-09T17:47:17Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T15:51:52.377308Z","submitted_at":"2025-01-09T17:47:17Z","title":"Generative Flow Networks: Theory and Applications to Structure 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:2501.05498."}