{"as_of":"2026-08-08T05:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:489b721df95167d448b3f7aa103fdb2c7986ca64230e0f36c86a6737047197bd","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T15:04:23.015735Z","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":"2026-08-07T11:23:00.149834Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.07930","last_updated":"2025-06-04T07:59:15Z","snapshot_observed_at":"2026-07-06T20:20:47.146343Z","submitted_at":"2025-01-14T08:32:12Z","title":"An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.07930","snapshot_observed_at":"2026-08-07T15:04:23.015735Z","title":"An adaptive orthogonal convolution scheme for efficient and flexible cnn architectures.arXiv preprint arXiv:2501.07930, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.16531","last_updated":"2025-09-10T10:50:10Z","snapshot_observed_at":"2026-08-07T21:09:49.874887Z","submitted_at":"2025-05-22T11:20:35Z","title":"HOFT: Householder Orthogonal Fine-tuning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:23.015735Z"},"links":{"cited_paper":"/paper/2501.07930","citing_paper":"/paper/2505.16531"},"observation_digest":"sha256:ee7753e7154ea5d0a5b75e3aae08afe9d8e5d19865383283d3a6920a7ac83be4","observation_id":"01a32f92-52b9-4cd1-a824-2344284c831f","resolution":{"observed_at":"2026-08-07T15:04:23.015735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07930","last_updated":"2025-06-04T07:59:15Z","snapshot_observed_at":"2026-07-06T20:20:47.146343Z","submitted_at":"2025-01-14T08:32:12Z","title":"An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures","version":3},"cited_work":{"arxiv_id":"2501.07930","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.07930","snapshot_observed_at":"2026-08-07T11:23:00.149834Z","title":"An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures","venue":"cs.AI","work_id":"9f81ad5c-fbb1-435d-9651-cbbc9ac03ac1","year":2025},"citing_paper":{"arxiv_id":"2506.02879","last_updated":"2025-08-09T03:52:46Z","snapshot_observed_at":"2026-08-07T22:16:11.657224Z","submitted_at":"2025-06-03T13:46:15Z","title":"Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T11:22:59.908342Z"},"links":{"cited_paper":"/paper/2501.07930","citing_paper":"/paper/2506.02879"},"observation_digest":"sha256:ce413e6c544ce69c42991301a409ede297affcb8ed0f6bef33f480e3976c581c","observation_id":"e281bb8d-cd54-4fdf-8756-689cd37d2a7b","resolution":{"observed_at":"2026-08-07T11:23:00.156267Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.07930/citation-record","integrity":"/paper/2501.07930/integrity","json":"/paper/2501.07930/citation-record.json","paper":"/paper/2501.07930"},"outbound":[],"paper":{"arxiv_id":"2501.07930","last_updated":"2025-06-04T07:59:15Z","latest_version":3,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T20:20:47.146343Z","submitted_at":"2025-01-14T08:32:12Z","title":"An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.07930."}