{"as_of":"2026-08-18T05:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8250ccf8e366248cdfd954b81a599e821e8d899c1c76b21ff6e8d434df887253","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":"2026-08-17T06:30:58.91139+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":"2026-08-08T19:02:30.547640Z","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":"2026-08-08T19:02:30.800032Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1811.07745","last_updated":"2018-11-19T15:15:18Z","snapshot_observed_at":"2026-08-17T03:08:51.789083Z","submitted_at":"2018-11-19T15:15:18Z","title":"Reinforcement Learning with A* and a Deep Heuristic","version":1},"cited_work":{"arxiv_id":"1811.07745","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.07745","snapshot_observed_at":"2026-08-08T19:02:30.800032Z","title":"Reinforcement Learning with A* and a Deep Heuristic","venue":"cs.LG","work_id":"9a2261d7-6441-4947-aaee-26334067a220","year":2018},"citing_paper":{"arxiv_id":"2502.05526","last_updated":"2025-02-08T11:13:07Z","snapshot_observed_at":"2026-08-16T11:31:25.422867Z","submitted_at":"2025-02-08T11:13:07Z","title":"Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T19:02:30.547640Z"},"links":{"cited_paper":"/paper/1811.07745","citing_paper":"/paper/2502.05526"},"observation_digest":"sha256:eec763498e1c090e8cc473e514a9f5a0822c1a9cbabf5de81f1d9f025a2744e9","observation_id":"22ee8b1d-b92f-4217-946a-b3b9d8c391ea","resolution":{"observed_at":"2026-08-08T19:02:30.805003Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1811.07745/citation-record","integrity":"/paper/1811.07745/integrity","json":"/paper/1811.07745/citation-record.json","paper":"/paper/1811.07745"},"outbound":[],"paper":{"arxiv_id":"1811.07745","last_updated":"2018-11-19T15:15:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T03:08:51.789083Z","submitted_at":"2018-11-19T15:15:18Z","title":"Reinforcement Learning with A* and a Deep Heuristic"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1811.07745."}