{"as_of":"2026-08-04T16:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:74179482e584b00477848b17cc8941037f77093a91c5fdaf4153e85884ab5a07","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":"2026-08-04T06:34:03.388597+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":"2026-06-28T02:35:08.323804Z","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":"2026-07-02T12:06:55.519808Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.13655","last_updated":"2024-06-19T15:58:35Z","snapshot_observed_at":"2026-07-06T18:33:45.816337Z","submitted_at":"2024-06-19T15:58:35Z","title":"Improving GFlowNets with Monte Carlo Tree Search","version":1},"cited_work":{"arxiv_id":"2406.13655","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.13655","snapshot_observed_at":"2026-07-02T12:06:55.519808Z","title":"arXiv preprint arXiv:2406.13655 , year=","venue":null,"work_id":"639b12d0-144b-4cb0-b25d-98dba35bc66b","year":null},"citing_paper":{"arxiv_id":"2606.06272","last_updated":"2026-06-04T15:14:24Z","snapshot_observed_at":"2026-07-06T23:46:10.612537Z","submitted_at":"2026-06-04T15:14:24Z","title":"Your GFlowNet Secretly Learns an Optimal Transport Plan","version":1},"reference_index":131,"source":"arxiv_source","source_observed_at":"2026-06-28T02:35:08.323804Z"},"links":{"cited_paper":"/paper/2406.13655","citing_paper":"/paper/2606.06272"},"observation_digest":"sha256:c1442ef689740a561b49aeeaa9f5bf672f05cfc29ad9c198dc98e46af8b18cfd","observation_id":"5c544b32-7af4-45cb-9988-2ea0331c36bd","resolution":{"observed_at":"2026-07-02T12:06:55.521390Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.13655/citation-record","integrity":"/paper/2406.13655/integrity","json":"/paper/2406.13655/citation-record.json","paper":"/paper/2406.13655"},"outbound":[],"paper":{"arxiv_id":"2406.13655","last_updated":"2024-06-19T15:58:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:33:45.816337Z","submitted_at":"2024-06-19T15:58:35Z","title":"Improving GFlowNets with Monte Carlo Tree Search"},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.13655."}