{"as_of":"2026-08-12T11:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1fccf8288d65bd70573850537556c83379ff20bdae92b691d860da63a680835","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":"2026-08-12T06:34:41.77262+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":"2026-08-11T13:10:41.956818Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T11:01:30.663330Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.14897","last_updated":"2023-11-30T04:13:59Z","snapshot_observed_at":"2026-08-10T14:02:37.924066Z","submitted_at":"2023-11-25T01:45:09Z","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.14897","snapshot_observed_at":"2026-08-11T13:10:41.956818Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13461","last_updated":"2025-03-10T15:25:59Z","snapshot_observed_at":"2026-08-11T20:00:19.287429Z","submitted_at":"2024-12-18T03:14:11Z","title":"Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T13:10:41.956818Z"},"links":{"cited_paper":"/paper/2311.14897","citing_paper":"/paper/2412.13461"},"observation_digest":"sha256:d7c15101e9176d298f834a7fb6325345930d6f7ebf368689b0b985143846c83a","observation_id":"8695b823-92ed-4045-80ce-e4dd68b8a606","resolution":{"observed_at":"2026-08-11T13:10:41.956818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.14897","last_updated":"2023-11-30T04:13:59Z","snapshot_observed_at":"2026-08-10T14:02:37.924066Z","submitted_at":"2023-11-25T01:45:09Z","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.14897","snapshot_observed_at":"2026-08-11T12:07:51.255617Z","title":"Towards scalable 3d anomaly detec- tion and localization: A benchmark via 3d anomaly synthe- sis and a self-supervised learning network","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14592","last_updated":"2024-12-19T07:23:17Z","snapshot_observed_at":"2026-08-11T12:03:22.884562Z","submitted_at":"2024-12-19T07:23:17Z","title":"Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T12:07:51.255617Z"},"links":{"cited_paper":"/paper/2311.14897","citing_paper":"/paper/2412.14592"},"observation_digest":"sha256:74298fc925e90135f47952fa22fcbd6be443a24ec127f4c3a83217edd0c5dbdc","observation_id":"3d73880a-84ac-4439-b0d0-7adbd9a0a09c","resolution":{"observed_at":"2026-08-11T12:07:51.255617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.14897","last_updated":"2023-11-30T04:13:59Z","snapshot_observed_at":"2026-08-10T14:02:37.924066Z","submitted_at":"2023-11-25T01:45:09Z","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","version":3},"cited_work":{"arxiv_id":"2311.14897","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.14897","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6e6dc6d9-d52d-44f1-90ce-c83ff732a606","year":2023},"citing_paper":{"arxiv_id":"2605.03437","last_updated":"2026-05-06T11:25:15Z","snapshot_observed_at":"2026-08-11T11:25:11.804820Z","submitted_at":"2026-05-05T07:16:47Z","title":"Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-07T17:57:53.482855Z"},"links":{"cited_paper":"/paper/2311.14897","citing_paper":"/paper/2605.03437"},"observation_digest":"sha256:858e7bd30761102f733361ac43b2e8f81232523b530565daab00e4be0dc51e87","observation_id":"fa9b6c10-e067-41c3-b5cf-cae4957f4500","resolution":{"observed_at":"2026-05-12T11:01:30.665841Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.14897","last_updated":"2023-11-30T04:13:59Z","snapshot_observed_at":"2026-08-10T14:02:37.924066Z","submitted_at":"2023-11-25T01:45:09Z","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","version":3},"cited_work":{"arxiv_id":"2311.14897","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.14897","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6e6dc6d9-d52d-44f1-90ce-c83ff732a606","year":2023},"citing_paper":{"arxiv_id":"2605.03437","last_updated":"2026-05-06T11:25:15Z","snapshot_observed_at":"2026-08-11T11:25:11.804820Z","submitted_at":"2026-05-05T07:16:47Z","title":"Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-08T19:20:50.624344Z"},"links":{"cited_paper":"/paper/2311.14897","citing_paper":"/paper/2605.03437"},"observation_digest":"sha256:92d93afae46772712836e589b612c35a66436f9a06d0629db36efd35923781b5","observation_id":"01f1d7b0-15a2-462d-8be7-c991e783fa27","resolution":{"observed_at":"2026-05-09T05:55:31.311584Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2311.14897/citation-record","integrity":"/paper/2311.14897/integrity","json":"/paper/2311.14897/citation-record.json","paper":"/paper/2311.14897"},"outbound":[],"paper":{"arxiv_id":"2311.14897","last_updated":"2023-11-30T04:13:59Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T14:02:37.924066Z","submitted_at":"2023-11-25T01:45:09Z","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2311.14897."}