{"as_of":"2026-08-08T04:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b21cd7add0a54f0ea8b9076fbaf55003006d139267872446c95ce24030c6fec0","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T05:04:03.960890Z","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":"2026-08-07T05:04:05.268725Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1511.05298","last_updated":"2016-04-11T19:00:24Z","snapshot_observed_at":"2026-07-06T04:36:44.657195Z","submitted_at":"2015-11-17T07:49:58Z","title":"Structural-RNN: Deep Learning on Spatio-Temporal Graphs","version":3},"cited_work":{"arxiv_id":"1511.05298","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05298","snapshot_observed_at":"2026-08-07T05:04:05.268725Z","title":"Structural-RNN: Deep Learning on Spatio-Temporal Graphs","venue":"cs.CV","work_id":"ad0d8fa7-df51-49d8-ba0c-7b4174573de5","year":2015},"citing_paper":{"arxiv_id":"2506.08963","last_updated":"2025-06-10T16:36:42Z","snapshot_observed_at":"2026-08-07T04:55:41.357391Z","submitted_at":"2025-06-10T16:36:42Z","title":"Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:04:03.960890Z"},"links":{"cited_paper":"/paper/1511.05298","citing_paper":"/paper/2506.08963"},"observation_digest":"sha256:3a19f988f056cc38c8d2ed795c29c4a605f2504b679f5f75c82ef04ea629840e","observation_id":"c65b5708-4687-4dc7-83fa-06df0facceb4","resolution":{"observed_at":"2026-08-07T05:04:05.354004Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1511.05298/citation-record","integrity":"/paper/1511.05298/integrity","json":"/paper/1511.05298/citation-record.json","paper":"/paper/1511.05298"},"outbound":[],"paper":{"arxiv_id":"1511.05298","last_updated":"2016-04-11T19:00:24Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T04:36:44.657195Z","submitted_at":"2015-11-17T07:49:58Z","title":"Structural-RNN: Deep Learning on Spatio-Temporal Graphs"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1511.05298."}