{"as_of":"2026-08-09T23:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dde385d92523f698b6027d51db46c3b793472e53a28128877544ee6681d14c0f","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:31:04.711501Z","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":"2026-08-06T21:45:14.335681Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1810.05795","last_updated":"2018-10-13T04:14:14Z","snapshot_observed_at":"2026-08-09T20:20:40.502989Z","submitted_at":"2018-10-13T04:14:14Z","title":"Point Cloud GAN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05795","snapshot_observed_at":"2026-08-07T14:31:04.711501Z","title":"Point cloud gan","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18819","last_updated":"2025-05-24T18:26:30Z","snapshot_observed_at":"2026-08-09T01:59:38.626593Z","submitted_at":"2025-05-24T18:26:30Z","title":"Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T14:31:04.711501Z"},"links":{"cited_paper":"/paper/1810.05795","citing_paper":"/paper/2505.18819"},"observation_digest":"sha256:0b0a55119ba45088f086df49a60f93fd9c4432d6739a8688e76b522899858377","observation_id":"a9748a4e-b614-42ea-a503-5354ddc573fc","resolution":{"observed_at":"2026-08-07T14:31:04.711501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05795","last_updated":"2018-10-13T04:14:14Z","snapshot_observed_at":"2026-08-09T20:20:40.502989Z","submitted_at":"2018-10-13T04:14:14Z","title":"Point Cloud GAN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05795","snapshot_observed_at":"2026-08-06T22:39:00.113039Z","title":"Point cloud gan.arXiv preprint arXiv:1810.05795, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.21076","last_updated":"2026-07-30T06:31:13Z","snapshot_observed_at":"2026-08-07T05:35:37.929373Z","submitted_at":"2025-06-26T08:03:14Z","title":"PoseMaster: A Unified 3D Native Framework for Stylized Pose Generation","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:39:00.113039Z"},"links":{"cited_paper":"/paper/1810.05795","citing_paper":"/paper/2506.21076"},"observation_digest":"sha256:089d5c34d4a0961878a81fc2661f73c1497cbf4ba21bcba79cd4f8552cdb5251","observation_id":"abbe7d9b-717b-4f32-bce8-9948a187a821","resolution":{"observed_at":"2026-08-06T22:39:00.113039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05795","last_updated":"2018-10-13T04:14:14Z","snapshot_observed_at":"2026-08-09T20:20:40.502989Z","submitted_at":"2018-10-13T04:14:14Z","title":"Point Cloud GAN","version":1},"cited_work":{"arxiv_id":"1810.05795","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.05795","snapshot_observed_at":"2026-08-06T21:45:14.335681Z","title":"Point Cloud GAN","venue":"cs.LG","work_id":"b473f266-44a0-4643-b502-c50d91a9285c","year":2018},"citing_paper":{"arxiv_id":"2506.23478","last_updated":"2025-06-30T02:53:40Z","snapshot_observed_at":"2026-08-09T20:20:26.145917Z","submitted_at":"2025-06-30T02:53:40Z","title":"GeoCD: A Differential Local Approximation for Geodesic Chamfer Distance","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:45:12.288122Z"},"links":{"cited_paper":"/paper/1810.05795","citing_paper":"/paper/2506.23478"},"observation_digest":"sha256:99adbda7b83d9cff160ffd8d5cbace668e2b80f8a5f0659c2805a96aacaae9ba","observation_id":"bfc9daee-8ea7-4a51-9c7f-7550fc28059a","resolution":{"observed_at":"2026-08-06T21:45:14.431255Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05795","last_updated":"2018-10-13T04:14:14Z","snapshot_observed_at":"2026-08-09T20:20:40.502989Z","submitted_at":"2018-10-13T04:14:14Z","title":"Point Cloud GAN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05795","snapshot_observed_at":"2026-08-01T09:54:36.521337Z","title":"arXiv preprint arXiv:1810.05795 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20642","last_updated":"2026-07-22T18:13:08Z","snapshot_observed_at":"2026-08-07T03:06:05.848372Z","submitted_at":"2026-07-22T18:13:08Z","title":"Masked Topology Modeling for Self-Supervised Learning on Parametric CAD","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-01T09:54:36.521337Z"},"links":{"cited_paper":"/paper/1810.05795","citing_paper":"/paper/2607.20642"},"observation_digest":"sha256:a6d02a863ba7cf62de3ea6ef8d91d3d23ff66fd87dd398097cfbff9249beb011","observation_id":"5cce7404-29da-4604-ba75-812f0c580a0c","resolution":{"observed_at":"2026-08-01T09:54:36.521337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1810.05795/citation-record","integrity":"/paper/1810.05795/integrity","json":"/paper/1810.05795/citation-record.json","paper":"/paper/1810.05795"},"outbound":[],"paper":{"arxiv_id":"1810.05795","last_updated":"2018-10-13T04:14:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T20:20:40.502989Z","submitted_at":"2018-10-13T04:14:14Z","title":"Point Cloud GAN"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1810.05795."}