{"as_of":"2026-08-09T07:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:009baa51fdaf217370d6f5425969b4dbf18fbe727a238739ac76f0737837988f","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T19:40:07.518385Z","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":"2026-07-02T16:27:09.712495Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1801.00173","last_updated":"2018-01-16T08:54:12Z","snapshot_observed_at":"2026-08-08T10:21:14.704361Z","submitted_at":"2017-12-30T18:27:35Z","title":"Theory of Deep Learning III: explaining the non-overfitting puzzle","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00173","snapshot_observed_at":"2026-08-07T19:40:07.518385Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.10051","last_updated":"2025-02-17T15:30:22Z","snapshot_observed_at":"2026-08-08T15:22:06.356585Z","submitted_at":"2025-02-14T10:00:20Z","title":"ORI: O Routing Intelligence","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T19:40:07.518385Z"},"links":{"cited_paper":"/paper/1801.00173","citing_paper":"/paper/2502.10051"},"observation_digest":"sha256:c594659a4a1aa792ea31c50263d9c560004ee11c60752c03a80918e5c74748c1","observation_id":"8f302d72-f1b1-4472-a43b-44949bc5e6c1","resolution":{"observed_at":"2026-08-07T19:40:07.518385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00173","last_updated":"2018-01-16T08:54:12Z","snapshot_observed_at":"2026-08-08T10:21:14.704361Z","submitted_at":"2017-12-30T18:27:35Z","title":"Theory of Deep Learning III: explaining the non-overfitting puzzle","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00173","snapshot_observed_at":"2026-08-03T15:02:51.402132Z","title":"Theory of deep learning iii: Explaining the non-overfitting puzzle,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.18471","last_updated":"2026-06-23T21:30:15Z","snapshot_observed_at":"2026-08-05T15:24:59.125196Z","submitted_at":"2025-12-20T19:10:38Z","title":"The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T15:02:51.402132Z"},"links":{"cited_paper":"/paper/1801.00173","citing_paper":"/paper/2512.18471"},"observation_digest":"sha256:4f955791237449834985e1b3d0d9ec1e9f550248a6ba0d55a3709833751a0952","observation_id":"45591153-5370-4639-b4a9-67541276db72","resolution":{"observed_at":"2026-08-03T15:02:51.402132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00173","last_updated":"2018-01-16T08:54:12Z","snapshot_observed_at":"2026-08-08T10:21:14.704361Z","submitted_at":"2017-12-30T18:27:35Z","title":"Theory of Deep Learning III: explaining the non-overfitting puzzle","version":2},"cited_work":{"arxiv_id":"1801.00173","doi":null,"metadata_source":"pith","pith_arxiv_id":"1801.00173","snapshot_observed_at":"2026-07-02T16:27:09.712495Z","title":"Theory of deep learning iii: Explaining the non-overfitting puzzle","venue":"cs.LG","work_id":"30845d72-303e-4fb6-a06a-f9f8495862bc","year":2017},"citing_paper":{"arxiv_id":"2605.06992","last_updated":"2026-05-07T22:16:03Z","snapshot_observed_at":"2026-07-06T23:19:25.538514Z","submitted_at":"2026-05-07T22:16:03Z","title":"Why Does Agentic Safety Fail to Generalize Across Tasks?","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-05-11T01:55:38.554161Z"},"links":{"cited_paper":"/paper/1801.00173","citing_paper":"/paper/2605.06992"},"observation_digest":"sha256:a06bf28e8fcfb484e0b20007ea145cdc80431219349e4c35945a453ac7467d53","observation_id":"ad4f6c86-9fb6-4868-9484-b091083cc8dd","resolution":{"observed_at":"2026-05-11T04:05:59.982076Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00173","last_updated":"2018-01-16T08:54:12Z","snapshot_observed_at":"2026-08-08T10:21:14.704361Z","submitted_at":"2017-12-30T18:27:35Z","title":"Theory of Deep Learning III: explaining the non-overfitting puzzle","version":2},"cited_work":{"arxiv_id":"1801.00173","doi":null,"metadata_source":"pith","pith_arxiv_id":"1801.00173","snapshot_observed_at":"2026-07-02T16:27:09.712495Z","title":"Theory of deep learning iii: Explaining the non-overfitting puzzle","venue":"cs.LG","work_id":"30845d72-303e-4fb6-a06a-f9f8495862bc","year":2017},"citing_paper":{"arxiv_id":"2606.06861","last_updated":"2026-06-05T03:11:08Z","snapshot_observed_at":"2026-08-09T05:17:49.724719Z","submitted_at":"2026-06-05T03:11:08Z","title":"Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T22:36:40.257282Z"},"links":{"cited_paper":"/paper/1801.00173","citing_paper":"/paper/2606.06861"},"observation_digest":"sha256:d557699a08aa3635a788047dbe5cf80f7dd94b23019616c9fbf1dad72f208adc","observation_id":"7c9050ec-30e2-4ce3-8f9a-d0e3013a90ab","resolution":{"observed_at":"2026-07-02T16:27:09.715135Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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/1801.00173/citation-record","integrity":"/paper/1801.00173/integrity","json":"/paper/1801.00173/citation-record.json","paper":"/paper/1801.00173"},"outbound":[],"paper":{"arxiv_id":"1801.00173","last_updated":"2018-01-16T08:54:12Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T10:21:14.704361Z","submitted_at":"2017-12-30T18:27:35Z","title":"Theory of Deep Learning III: explaining the non-overfitting puzzle"},"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 4 inbound Pith citation observations for arXiv:1801.00173."}