{"as_of":"2026-08-09T13:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ebe5cb7649d5a35ed91e17e8edefbde30a9a1897f07e6ab0f949fd2835475320","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T00:32:57.552039Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":80,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.06717","last_updated":"2022-04-02T09:52:27Z","snapshot_observed_at":"2026-08-07T16:18:01.626262Z","submitted_at":"2022-03-13T17:22:44Z","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06717","snapshot_observed_at":"2026-08-07T00:32:57.552039Z","title":"Scaling up your kernels to 31×31: Revisiting large kernel design in CNNs,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.14846","last_updated":"2025-06-16T15:15:30Z","snapshot_observed_at":"2026-08-09T12:15:18.767237Z","submitted_at":"2025-06-16T15:15:30Z","title":"Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T00:32:57.552039Z"},"links":{"cited_paper":"/paper/2203.06717","citing_paper":"/paper/2506.14846"},"observation_digest":"sha256:fadc41a09792e2dc70238f4c18f24233ff4d81c776e6840534441b168bf794a2","observation_id":"9501e7b5-b298-44ae-9347-e26b2435db97","resolution":{"observed_at":"2026-08-07T00:32:57.552039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06717","last_updated":"2022-04-02T09:52:27Z","snapshot_observed_at":"2026-08-07T16:18:01.626262Z","submitted_at":"2022-03-13T17:22:44Z","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs","version":4},"cited_work":{"arxiv_id":"2203.06717","doi":"10.48550/arxiv.2203.06717","metadata_source":"pith","pith_arxiv_id":"2203.06717","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs","venue":"cs.CV","work_id":"17ab6d18-eb7f-4c1f-ab13-7770cd6785e3","year":2022},"citing_paper":{"arxiv_id":"2607.22139","last_updated":"2026-07-24T09:35:18Z","snapshot_observed_at":"2026-08-01T05:42:50.857859Z","submitted_at":"2026-07-24T09:35:18Z","title":"CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T05:42:56.893239Z"},"links":{"cited_paper":"/paper/2203.06717","citing_paper":"/paper/2607.22139"},"observation_digest":"sha256:b10e915c40615154117dbb7ef7f9ae27aa7d5313b239a76ed519690fd11a39c2","observation_id":"7c69ffde-c6b7-4330-adbe-fbf6ec341694","resolution":{"observed_at":"2026-08-01T05:43:58.125340Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2203.06717/citation-record","integrity":"/paper/2203.06717/integrity","json":"/paper/2203.06717/citation-record.json","paper":"/paper/2203.06717"},"outbound":[],"paper":{"arxiv_id":"2203.06717","last_updated":"2022-04-02T09:52:27Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T16:18:01.626262Z","submitted_at":"2022-03-13T17:22:44Z","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs"},"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 2 inbound Pith citation observations for arXiv:2203.06717."}