{"as_of":"2026-08-21T07:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b626225f933391a14a3b894af5abfa320996c8e7e0a1bdce061812e49e77f35c","coverage":[{"denominator":6,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:45:12.921086Z","state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-08-15T17:52:30.579434Z","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":"2026-08-15T18:16:14.067578Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"cited_work":{"arxiv_id":"2506.19375","doi":"10.48550/arxiv.2506.19375","metadata_source":"pith","pith_arxiv_id":"2506.19375","snapshot_observed_at":"2026-08-15T18:16:14.067578Z","title":"Path Learning with Trajectory Advantage Regression","venue":"cs.LG","work_id":"6d001048-65d5-46dd-9db2-35a943cb34b2","year":2025},"citing_paper":{"arxiv_id":"2508.00895","last_updated":"2025-07-27T20:23:09Z","snapshot_observed_at":"2026-08-20T19:41:33.762862Z","submitted_at":"2025-07-27T20:23:09Z","title":"Cross-Process Defect Attribution using Potential Loss Analysis","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T17:52:30.579434Z"},"links":{"cited_paper":"/paper/2506.19375","citing_paper":"/paper/2508.00895"},"observation_digest":"sha256:6c498665f8ab917f195d72c8e89ef40074f74a2677bd25e3a389d736056f73a9","observation_id":"1ff0286d-deb8-4928-82f8-7b68c8bea277","resolution":{"observed_at":"2026-08-15T17:52:30.662070Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.19375/citation-record","integrity":"/paper/2506.19375/integrity","json":"/paper/2506.19375/citation-record.json","paper":"/paper/2506.19375"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.00340","last_updated":"2024-03-30T12:37:58Z","snapshot_observed_at":"2026-08-16T14:05:00.944596Z","submitted_at":"2024-03-30T12:37:58Z","title":"Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00340","snapshot_observed_at":"2026-08-15T18:45:12.893516Z","title":"Deep reinforcement learning in autonomous car path planning and control: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T18:45:12.893516Z"},"links":{"cited_paper":"/paper/2404.00340","citing_paper":"/paper/2506.19375"},"observation_digest":"sha256:8e0aedd846809b1264a5c06eaf57b31bab1c6a576a4c4b818a2067cea27fa48f","observation_id":"e2b9a64a-5c44-4cb8-8271-5e2e1988bd93","resolution":{"observed_at":"2026-08-15T18:45:12.893516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:45:13.025012Z","title":"Trajectory regression on road networks","venue":null,"work_id":"f47a022c-a851-47bb-a655-07d3501fbf43","year":2011},"citing_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T18:45:12.900144Z"},"links":{"citing_paper":"/paper/2506.19375"},"observation_digest":"sha256:dca5886c1587e3b8d5c17c40cc3b5c6f440088a8f332fa4135064aa23e32f31f","observation_id":"396b2bf6-8c69-4897-99a4-ce7c7ababe77","resolution":{"observed_at":"2026-08-15T18:45:13.030212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01643","last_updated":"2020-11-01T23:50:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-04T17:00:15Z","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.01643","snapshot_observed_at":"2026-08-15T18:45:12.905318Z","title":"Offline reinforcement learning: Tutorial, review, and perspectives on open problems","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T18:45:12.905318Z"},"links":{"cited_paper":"/paper/2005.01643","citing_paper":"/paper/2506.19375"},"observation_digest":"sha256:3404a5265dbd6596cdfe2e7d76100a24d2415d797b1b6070b95700931e4ce0af","observation_id":"fde59e99-b960-4f9d-a840-83b5a9a2e9fd","resolution":{"observed_at":"2026-08-15T18:45:12.905318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:45:13.006941Z","title":"Route planning under uncertainty: The canadian traveller problem","venue":null,"work_id":"a4640ce3-2af7-4ee3-8f12-27a595b902dc","year":2008},"citing_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T18:45:12.910695Z"},"links":{"citing_paper":"/paper/2506.19375"},"observation_digest":"sha256:53aa09ea524528ce47a7ea2824e0569b0a41c76a6ec537dfa868426861f3f635","observation_id":"b0d690a3-eadc-4d13-ac4d-835179c02ca9","resolution":{"observed_at":"2026-08-15T18:45:13.013764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:45:12.916011Z","title":"Markov decision processes: discrete stochastic dynamic programming","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T18:45:12.916011Z"},"links":{"citing_paper":"/paper/2506.19375"},"observation_digest":"sha256:8280187eb365e777d5a419caa6b22b59cd7d069a1089ee9464689c5711d663a8","observation_id":"4b5eb5c2-55ac-4d6d-bcb5-9b85f4ba092c","resolution":{"observed_at":"2026-08-15T18:45:12.916011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:45:12.921086Z","title":"Reinforcement learning: An introduction , volume 1","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T18:45:12.921086Z"},"links":{"citing_paper":"/paper/2506.19375"},"observation_digest":"sha256:da41b7e7ff16c45b14e685098e4111364de6c6c0bb38e89226ab24bc93a7b450","observation_id":"140dce04-a6c6-49a1-8e5f-b42c43e32dcc","resolution":{"observed_at":"2026-08-15T18:45:12.921086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.19375","last_updated":"2025-06-24T07:05:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T19:41:20.003164Z","submitted_at":"2025-06-24T07:05:23Z","title":"Path Learning with Trajectory Advantage Regression"},"reference_resolution":{"displayed":6,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":6},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 1 inbound Pith citation observation for arXiv:2506.19375."}