{"as_of":"2026-08-10T08:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:21974440573da16ebf953551c6f74e3340205e6670c7b5e65e0caaa1b658ec08","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":"2026-08-10T06:31:04.303077+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":"2026-08-06T19:44:36.405794Z","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":"2026-08-06T19:44:37.181425Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2304.06911","last_updated":"2024-04-28T18:36:19Z","snapshot_observed_at":"2026-08-10T04:01:02.905393Z","submitted_at":"2023-04-14T03:25:24Z","title":"3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining","version":2},"cited_work":{"arxiv_id":"2304.06911","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.06911","snapshot_observed_at":"2026-08-06T19:44:37.181425Z","title":"3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining","venue":"cs.CV","work_id":"f9d3939a-c422-46aa-865e-092479dc04a2","year":2023},"citing_paper":{"arxiv_id":"2507.04801","last_updated":"2025-07-08T08:17:37Z","snapshot_observed_at":"2026-08-06T19:36:54.438128Z","submitted_at":"2025-07-07T09:21:28Z","title":"PointGAC: Geometric-Aware Codebook for Masked Point Cloud Modeling","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:44:36.405794Z"},"links":{"cited_paper":"/paper/2304.06911","citing_paper":"/paper/2507.04801"},"observation_digest":"sha256:18eb83572b4ffefe447df8d8e22e508daf7255aa6f808f67d45743e55b185b3b","observation_id":"cbdafc7d-520c-4568-b174-d40d50a163be","resolution":{"observed_at":"2026-08-06T19:44:37.240389Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2304.06911/citation-record","integrity":"/paper/2304.06911/integrity","json":"/paper/2304.06911/citation-record.json","paper":"/paper/2304.06911"},"outbound":[],"paper":{"arxiv_id":"2304.06911","last_updated":"2024-04-28T18:36:19Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T04:01:02.905393Z","submitted_at":"2023-04-14T03:25:24Z","title":"3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2304.06911."}