{"as_of":"2026-08-08T21:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:01ba06dd29e70083bccad3599989a2e916c87c81c898c7df243ff85aae829b20","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:11:41.659284Z","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":"2026-05-21T08:59:55.462547Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-08-07T13:11:41.659284Z","title":"Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.659284Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:061d6493515ae39b329621bb70050c8d2389b54814d4ad6d36e3c0755665de23","observation_id":"ff43acea-cec6-42d2-ace6-87bfd7ce9a13","resolution":{"observed_at":"2026-08-07T13:11:41.659284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-08-07T05:09:46.144332Z","title":"Gaugeequiv- ariant mesh cnns: Anisotropic convolutions on geometric graphs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08729","last_updated":"2025-06-11T13:26:10Z","snapshot_observed_at":"2026-08-08T10:36:57.905177Z","submitted_at":"2025-06-10T12:27:12Z","title":"Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:09:46.144332Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2506.08729"},"observation_digest":"sha256:e0bbcfbd8106c3be6a0264d9dbfa7845a8f7d308ea5657ae3fe3e86562e0419f","observation_id":"d014ebd2-2f13-4493-b03b-7d6affef0426","resolution":{"observed_at":"2026-08-07T05:09:46.144332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":"2003.05425","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs","venue":null,"work_id":"63494a93-5bf7-408a-a7f0-367d3d6a43a1","year":2003},"citing_paper":{"arxiv_id":"2605.06395","last_updated":"2026-05-19T20:23:58Z","snapshot_observed_at":"2026-08-08T10:15:30.750851Z","submitted_at":"2026-05-07T15:08:58Z","title":"Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T12:51:29.926471Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2605.06395"},"observation_digest":"sha256:e9a26045e983a8402f072cc61b958c46c4c9e6b57e9d0553f1cf818c6c144ba8","observation_id":"398d14a8-a43f-48c6-be5c-7db3c003cff9","resolution":{"observed_at":"2026-05-11T19:01:19.090661Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":"2003.05425","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs","venue":null,"work_id":"63494a93-5bf7-408a-a7f0-367d3d6a43a1","year":2003},"citing_paper":{"arxiv_id":"2605.06395","last_updated":"2026-05-19T20:23:58Z","snapshot_observed_at":"2026-08-08T10:15:30.750851Z","submitted_at":"2026-05-07T15:08:58Z","title":"Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-21T08:57:04.570689Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2605.06395"},"observation_digest":"sha256:488eb06cc07b90bafa0aeac3a141774f7274723e07ecf9c2e80e0051aea6b978","observation_id":"aae06787-1671-46be-b473-e64f01b3044c","resolution":{"observed_at":"2026-05-21T08:59:55.464530Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":"2003.05425","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs","venue":null,"work_id":"63494a93-5bf7-408a-a7f0-367d3d6a43a1","year":2003},"citing_paper":{"arxiv_id":"2605.08172","last_updated":"2026-05-04T23:54:07Z","snapshot_observed_at":"2026-08-02T06:45:35.064748Z","submitted_at":"2026-05-04T23:54:07Z","title":"Augmented Equivariant Mesh Networks for Anatomical Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-12T01:25:11.093369Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2605.08172"},"observation_digest":"sha256:3fa6453d02abb5fbfc32e69cdc6233212d56907e9bdb219e28ff985f56ee047a","observation_id":"a6aa6bc7-5f2b-4be9-823f-abaa3759c6de","resolution":{"observed_at":"2026-05-12T08:01:27.770357Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-07-31T05:06:54.931417Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24954","last_updated":"2026-07-27T18:04:26Z","snapshot_observed_at":"2026-08-01T00:03:34.329535Z","submitted_at":"2026-07-27T18:04:26Z","title":"Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-31T05:06:54.931417Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2607.24954"},"observation_digest":"sha256:df94b26281773b7edabd1b5f673b272c81824db72fbf4ce073e0944ebdd9ddbe","observation_id":"421444e9-2374-4d9e-9d4b-8a9a2611c768","resolution":{"observed_at":"2026-07-31T05:06:54.931417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2003.05425/citation-record","integrity":"/paper/2003.05425/integrity","json":"/paper/2003.05425/citation-record.json","paper":"/paper/2003.05425"},"outbound":[],"paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T11:53:57.880860Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric 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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2003.05425."}