{"as_of":"2026-08-17T16:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ada6120d0187ec10f76e225df5757ad367f4ae4e6c5beb0375b020a482538b3","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":"2026-08-17T06:30:58.91139+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":"2026-08-15T21:17:27.204739Z","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":"2026-08-15T17:49:09.743580Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.04556","last_updated":"2023-08-08T20:06:12Z","snapshot_observed_at":"2026-08-16T15:09:51.763210Z","submitted_at":"2023-08-08T20:06:12Z","title":"FocalFormer3D : Focusing on Hard Instance for 3D Object Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.04556","snapshot_observed_at":"2026-08-15T21:17:27.204739Z","title":"Focal- Former3D : Focusing on Hard Instance for 3D Object Detec- tion, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10258","last_updated":"2025-05-15T13:09:19Z","snapshot_observed_at":"2026-08-17T04:19:23.931470Z","submitted_at":"2025-05-15T13:09:19Z","title":"Inferring Driving Maps by Deep Learning-based Trail Map Extraction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:17:27.204739Z"},"links":{"cited_paper":"/paper/2308.04556","citing_paper":"/paper/2505.10258"},"observation_digest":"sha256:304fa115ed52261db419de0ef7c74b33be4ee633c203da60088b7d91f3bd6227","observation_id":"28bf96bf-9aac-4bc2-adc2-37e755fe816d","resolution":{"observed_at":"2026-08-15T21:17:27.204739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.04556","last_updated":"2023-08-08T20:06:12Z","snapshot_observed_at":"2026-08-16T15:09:51.763210Z","submitted_at":"2023-08-08T20:06:12Z","title":"FocalFormer3D : Focusing on Hard Instance for 3D Object Detection","version":1},"cited_work":{"arxiv_id":"2308.04556","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.04556","snapshot_observed_at":"2026-08-15T17:49:09.743580Z","title":"FocalFormer3D : Focusing on Hard Instance for 3D Object Detection","venue":"cs.CV","work_id":"ff80a5e2-e913-4653-a03e-2b161d375235","year":2023},"citing_paper":{"arxiv_id":"2507.20438","last_updated":"2025-07-27T23:15:13Z","snapshot_observed_at":"2026-08-16T00:21:54.853198Z","submitted_at":"2025-07-27T23:15:13Z","title":"Teleoperating Autonomous Vehicles over Commercial 5G Networks: Are We There Yet?","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T17:49:08.059947Z"},"links":{"cited_paper":"/paper/2308.04556","citing_paper":"/paper/2507.20438"},"observation_digest":"sha256:61cc054e8be848a6bdf38eff7e1bf1a598a513b138ef378591aaabfef2993290","observation_id":"b8f1a354-44b6-46b5-8e0d-65d33f378386","resolution":{"observed_at":"2026-08-15T17:49:09.751036Z","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/2308.04556/citation-record","integrity":"/paper/2308.04556/integrity","json":"/paper/2308.04556/citation-record.json","paper":"/paper/2308.04556"},"outbound":[],"paper":{"arxiv_id":"2308.04556","last_updated":"2023-08-08T20:06:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T15:09:51.763210Z","submitted_at":"2023-08-08T20:06:12Z","title":"FocalFormer3D : Focusing on Hard Instance for 3D Object Detection"},"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 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.04556."}