{"as_of":"2026-08-16T21:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a0a95bd0b5064eb2bcc02eb6d8fbbe5e4f3b0f7719fa714d370b0bf530684108","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:04:40.465321Z","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":"2026-05-13T06:52:25.771729Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.11893","last_updated":"2025-06-07T06:10:47Z","snapshot_observed_at":"2026-08-16T12:57:40.899829Z","submitted_at":"2025-02-17T15:20:04Z","title":"Rethinking Benign Overfitting in Two-Layer Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11893","snapshot_observed_at":"2026-08-15T21:04:40.465321Z","title":"Rethinking benign overfitting in two-layer neural networks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11621","last_updated":"2025-05-16T18:37:51Z","snapshot_observed_at":"2026-08-16T07:39:04.028596Z","submitted_at":"2025-05-16T18:37:51Z","title":"A Classical View on Benign Overfitting: The Role of Sample Size","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-15T21:04:40.465321Z"},"links":{"cited_paper":"/paper/2502.11893","citing_paper":"/paper/2505.11621"},"observation_digest":"sha256:eaf271d894f9a9d5176ee85dc273f41bc1a4fdb41899bf86a731e82cf490af1c","observation_id":"4ef54f09-6523-4612-bf69-888eda171e7a","resolution":{"observed_at":"2026-08-15T21:04:40.465321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11893","last_updated":"2025-06-07T06:10:47Z","snapshot_observed_at":"2026-08-16T12:57:40.899829Z","submitted_at":"2025-02-17T15:20:04Z","title":"Rethinking Benign Overfitting in Two-Layer Neural Networks","version":2},"cited_work":{"arxiv_id":"2502.11893","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.11893","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2502.11893 , year=","venue":null,"work_id":"53562a80-d860-4b7b-a2b3-bfe26f180c18","year":null},"citing_paper":{"arxiv_id":"2605.06314","last_updated":"2026-05-12T05:32:02Z","snapshot_observed_at":"2026-08-14T13:05:09.818944Z","submitted_at":"2026-05-07T14:14:09Z","title":"When Does $\\ell_2$-Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the $\\ell_1$ Implicit Bias","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-08T13:12:58.988440Z"},"links":{"cited_paper":"/paper/2502.11893","citing_paper":"/paper/2605.06314"},"observation_digest":"sha256:fcb2f1a3c336395a4e310c266539652e3320f9420f6cb99eeecc4f0247461495","observation_id":"cd6c0127-e00e-4ab3-9953-5a37c5912c66","resolution":{"observed_at":"2026-05-11T18:56:07.664528Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11893","last_updated":"2025-06-07T06:10:47Z","snapshot_observed_at":"2026-08-16T12:57:40.899829Z","submitted_at":"2025-02-17T15:20:04Z","title":"Rethinking Benign Overfitting in Two-Layer Neural Networks","version":2},"cited_work":{"arxiv_id":"2502.11893","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.11893","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2502.11893 , year=","venue":null,"work_id":"53562a80-d860-4b7b-a2b3-bfe26f180c18","year":null},"citing_paper":{"arxiv_id":"2605.06314","last_updated":"2026-05-12T05:32:02Z","snapshot_observed_at":"2026-08-14T13:05:09.818944Z","submitted_at":"2026-05-07T14:14:09Z","title":"When Does $\\ell_2$-Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the $\\ell_1$ Implicit Bias","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-13T06:49:46.350099Z"},"links":{"cited_paper":"/paper/2502.11893","citing_paper":"/paper/2605.06314"},"observation_digest":"sha256:45a9bd49a2f1b6ec5b5f535eee20bec963f84ea05c785c0018df7983132f492f","observation_id":"e8e1f425-7c4c-4629-9b6a-772fc481230b","resolution":{"observed_at":"2026-05-13T06:52:25.774387Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.11893/citation-record","integrity":"/paper/2502.11893/integrity","json":"/paper/2502.11893/citation-record.json","paper":"/paper/2502.11893"},"outbound":[],"paper":{"arxiv_id":"2502.11893","last_updated":"2025-06-07T06:10:47Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T12:57:40.899829Z","submitted_at":"2025-02-17T15:20:04Z","title":"Rethinking Benign Overfitting in Two-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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.11893."}