{"as_of":"2026-08-18T01:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ac0b20a45f13c9ae3def8d60061ae715d4de968824318e6e9e17bc0a4690407d","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:03:43.349023Z","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":"2026-07-03T00:47:30.928829Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-08-14T11:03:43.349023Z","title":"CoRR abs/1905.12600 (2019), https://arxiv.org/pdf/1905.12600v1.pdf","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.09942","last_updated":"2019-08-26T22:32:33Z","snapshot_observed_at":"2026-08-16T11:46:11.808888Z","submitted_at":"2019-08-26T22:32:33Z","title":"On the Bounds of Function Approximations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T11:03:43.349023Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/1908.09942"},"observation_digest":"sha256:833eb36badb137a215bec1aa3b590b91cdafde2b56c787a798ea339bd5fb3f6e","observation_id":"20c19fc5-2d5d-4f58-8635-8ebb4abf98e5","resolution":{"observed_at":"2026-08-14T11:03:43.349023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":"1905.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-07-03T00:47:30.928829Z","title":"arXiv preprint arXiv:1905.12600 , year=","venue":null,"work_id":"85906468-8e60-4d99-a494-806a775287c5","year":1905},"citing_paper":{"arxiv_id":"2401.01335","last_updated":"2024-06-14T21:17:17Z","snapshot_observed_at":"2026-08-15T08:59:30.302596Z","submitted_at":"2024-01-02T18:53:13Z","title":"Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models","version":3},"reference_index":223,"source":"arxiv_source","source_observed_at":"2026-05-14T23:00:20.720030Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/2401.01335"},"observation_digest":"sha256:4ee2b2186b98776114237e0c75d64ddfd8a9fa5c41508707996defa9b078eabd","observation_id":"e813d611-b324-44ce-b4cd-b3f9c859330f","resolution":{"observed_at":"2026-05-14T23:00:20.934186Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-08-12T15:36:20.067992Z","title":"Implicit Bias of Linear Equivariant Networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14288","last_updated":"2025-01-23T22:12:32Z","snapshot_observed_at":"2026-08-17T21:54:16.462193Z","submitted_at":"2024-11-21T16:36:01Z","title":"On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-12T15:36:20.067992Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/2411.14288"},"observation_digest":"sha256:23f90facaf3d35946f0723c44d0fd016bc9b8bc1e26cfa5fa7852ac0a1d5ce32","observation_id":"ae25e59d-356f-4fc7-a805-13ad8e904755","resolution":{"observed_at":"2026-08-12T15:36:20.067992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-08-05T16:14:10.331509Z","title":"M., & Sedghi, H","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.18920","last_updated":"2025-08-26T10:47:59Z","snapshot_observed_at":"2026-08-09T01:51:48.938064Z","submitted_at":"2025-08-26T10:47:59Z","title":"Generalization Bound for a General Class of Neural Ordinary Differential Equations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T16:14:10.331509Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/2508.18920"},"observation_digest":"sha256:31420de214ddc24c61f9cdd3d7e9773da4c35909f3f506b3d49b7ecf399741be","observation_id":"86074d2a-5b0a-4176-b556-f8fc27bc43a6","resolution":{"observed_at":"2026-08-05T16:14:10.331509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":"1905.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-07-03T00:47:30.928829Z","title":"arXiv preprint arXiv:1905.12600 , year=","venue":null,"work_id":"85906468-8e60-4d99-a494-806a775287c5","year":1905},"citing_paper":{"arxiv_id":"2605.02116","last_updated":"2026-05-28T04:23:25Z","snapshot_observed_at":"2026-07-06T23:15:11.601042Z","submitted_at":"2026-05-04T00:38:29Z","title":"Statistical Consistency and Generalization of Contrastive Representation Learning","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-21T09:00:00.110353Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/2605.02116"},"observation_digest":"sha256:306f7272d4526ec1caf0afce66a486abd3736e37eae7cfc9615674910855017c","observation_id":"eb29b126-6a11-430d-959b-1e65dcc3b872","resolution":{"observed_at":"2026-05-21T09:04:04.653960Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":"1905.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-07-03T00:47:30.928829Z","title":"arXiv preprint arXiv:1905.12600 , year=","venue":null,"work_id":"85906468-8e60-4d99-a494-806a775287c5","year":1905},"citing_paper":{"arxiv_id":"2605.02116","last_updated":"2026-05-28T04:23:25Z","snapshot_observed_at":"2026-07-06T23:15:11.601042Z","submitted_at":"2026-05-04T00:38:29Z","title":"Statistical Consistency and Generalization of Contrastive Representation Learning","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-01T00:08:49.735309Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/2605.02116"},"observation_digest":"sha256:d2aaa25479e5e41697dd7496d572d3952816e5693d682b405c35a199fe97e56f","observation_id":"de3c2db5-1268-44f3-bd8d-ec2712672f72","resolution":{"observed_at":"2026-07-01T00:15:09.446888Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional neural networks","version":6},"cited_work":{"arxiv_id":"1905.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.12600","snapshot_observed_at":"2026-07-03T00:47:30.928829Z","title":"arXiv preprint arXiv:1905.12600 , year=","venue":null,"work_id":"85906468-8e60-4d99-a494-806a775287c5","year":1905},"citing_paper":{"arxiv_id":"2606.10089","last_updated":"2026-06-08T19:16:32Z","snapshot_observed_at":"2026-08-12T09:27:24.769295Z","submitted_at":"2026-06-08T19:16:32Z","title":"A Theory on Flow Matching with Neural Networks","version":1},"reference_index":242,"source":"arxiv_source","source_observed_at":"2026-06-27T16:59:34.084575Z"},"links":{"cited_paper":"/paper/1905.12600","citing_paper":"/paper/2606.10089"},"observation_digest":"sha256:0fa4c61b64faa2feae207a77a60312883d2d704328f96cae22369bc84b393886","observation_id":"20ea0ebd-eff8-4617-8910-c01f88d04c12","resolution":{"observed_at":"2026-07-03T00:47:30.930382Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1905.12600/citation-record","integrity":"/paper/1905.12600/integrity","json":"/paper/1905.12600/citation-record.json","paper":"/paper/1905.12600"},"outbound":[],"paper":{"arxiv_id":"1905.12600","last_updated":"2020-04-08T05:10:39Z","latest_version":6,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T16:24:17.903428Z","submitted_at":"2019-05-29T17:20:36Z","title":"Generalization bounds for deep convolutional 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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1905.12600."}