{"as_of":"2026-08-14T09:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c91167931cb45b89fb2a9a3d159d927e27094863cd9f981dd063587819f67848","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T18:06:13.038843Z","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":"2026-08-11T18:06:13.247822Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.16891","last_updated":"2023-09-29T07:17:50Z","snapshot_observed_at":"2026-08-13T11:32:29.110931Z","submitted_at":"2023-05-26T12:51:38Z","title":"Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks","version":2},"cited_work":{"arxiv_id":"2305.16891","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.16891","snapshot_observed_at":"2026-08-11T18:06:13.247822Z","title":"Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks","venue":"cs.LG","work_id":"abde4a79-0243-428f-942e-6cb21b35b84f","year":2023},"citing_paper":{"arxiv_id":"2412.08282","last_updated":"2024-12-19T06:35:21Z","snapshot_observed_at":"2026-08-13T09:26:15.735994Z","submitted_at":"2024-12-11T10:57:16Z","title":"How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T18:06:13.038843Z"},"links":{"cited_paper":"/paper/2305.16891","citing_paper":"/paper/2412.08282"},"observation_digest":"sha256:a0c2f5c5d29f98fd15efd6dab12f98898773795231ca89e1bb7293eb0b61fa00","observation_id":"5041dcf8-30ef-45d0-95e6-9ad9a1a0c9c8","resolution":{"observed_at":"2026-08-11T18:06:13.259522Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.16891/citation-record","integrity":"/paper/2305.16891/integrity","json":"/paper/2305.16891/citation-record.json","paper":"/paper/2305.16891"},"outbound":[],"paper":{"arxiv_id":"2305.16891","last_updated":"2023-09-29T07:17:50Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T11:32:29.110931Z","submitted_at":"2023-05-26T12:51:38Z","title":"Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2305.16891."}