{"as_of":"2026-08-16T09:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ae80b96f41902914aa6bc66d2d19e74ffac819ff77fa457ac8252649b3c0e94","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:44:36.712015Z","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":"2026-07-09T02:35:53.832702Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1305.4825","last_updated":"2016-09-17T09:42:53Z","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1305.4825","snapshot_observed_at":"2026-08-14T05:12:14.231175Z","title":"and Mendelson, S","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1909.02088","last_updated":"2021-04-08T22:02:42Z","snapshot_observed_at":"2026-08-16T07:15:10.445469Z","submitted_at":"2019-09-04T20:21:02Z","title":"On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T05:12:14.231175Z"},"links":{"cited_paper":"/paper/1305.4825","citing_paper":"/paper/1909.02088"},"observation_digest":"sha256:649067d4fa02d75235a207e98ec7a0114638a8bb64ec4e8a1e3b279939071dde","observation_id":"9ddd14e1-399b-4f77-8207-658a5b34b92e","resolution":{"observed_at":"2026-08-14T05:12:14.231175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.4825","last_updated":"2016-09-17T09:42:53Z","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1305.4825","snapshot_observed_at":"2026-08-12T18:06:35.969380Z","title":"Learning the Kernel Matrix with Semidefinite Programming","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.12029","last_updated":"2024-11-18T20:05:05Z","snapshot_observed_at":"2026-08-12T17:57:12.150281Z","submitted_at":"2024-11-18T20:05:05Z","title":"On the Efficiency of ERM in Feature Learning","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-12T18:06:35.969380Z"},"links":{"cited_paper":"/paper/1305.4825","citing_paper":"/paper/2411.12029"},"observation_digest":"sha256:1f82fb8bc245a690b94aa81b3088911a014702d91bf3d3d88390afd9b32a54c7","observation_id":"d8be3dec-c41c-45a8-a741-4fd6b1f89bf1","resolution":{"observed_at":"2026-08-12T18:06:35.969380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.4825","last_updated":"2016-09-17T09:42:53Z","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1305.4825","snapshot_observed_at":"2026-08-08T22:54:24.924833Z","title":"Lecu \\'e and S","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.04297","last_updated":"2025-02-06T18:39:03Z","snapshot_observed_at":"2026-08-16T04:54:00.316342Z","submitted_at":"2025-02-06T18:39:03Z","title":"Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T22:54:24.924833Z"},"links":{"cited_paper":"/paper/1305.4825","citing_paper":"/paper/2502.04297"},"observation_digest":"sha256:4396d5675cd539533ca5568b85a228d927a84c31e2393b06fb3950e5e9eac958","observation_id":"4dce5a64-e32c-436e-b8e0-01b49296c123","resolution":{"observed_at":"2026-08-08T22:54:24.924833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.4825","last_updated":"2016-09-17T09:42:53Z","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1305.4825","snapshot_observed_at":"2026-08-15T16:44:36.712015Z","title":"Lecu \\'e and S","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.02528","last_updated":"2025-09-02T17:29:23Z","snapshot_observed_at":"2026-08-16T05:02:48.008896Z","submitted_at":"2025-09-02T17:29:23Z","title":"Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T16:44:36.712015Z"},"links":{"cited_paper":"/paper/1305.4825","citing_paper":"/paper/2509.02528"},"observation_digest":"sha256:786787ae175441f443d78b712693978183063e118b26fbeec0f588c0254d68e0","observation_id":"85f2a205-f404-4abe-b49f-e97c7cff4591","resolution":{"observed_at":"2026-08-15T16:44:36.712015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.4825","last_updated":"2016-09-17T09:42:53Z","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds","version":2},"cited_work":{"arxiv_id":"1305.4825","doi":null,"metadata_source":"pith","pith_arxiv_id":"1305.4825","snapshot_observed_at":"2026-07-09T02:35:53.832702Z","title":"Learning subgaussian classes : Upper and minimax bounds","venue":"math.ST","work_id":"e86711f0-1c7f-4d80-b638-bded5ad25f47","year":2013},"citing_paper":{"arxiv_id":"2606.06418","last_updated":"2026-06-04T17:22:58Z","snapshot_observed_at":"2026-08-02T08:47:15.917924Z","submitted_at":"2026-06-04T17:22:58Z","title":"Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss","version":1},"reference_index":212,"source":"arxiv_source","source_observed_at":"2026-06-28T02:35:39.845487Z"},"links":{"cited_paper":"/paper/1305.4825","citing_paper":"/paper/2606.06418"},"observation_digest":"sha256:d737525ee7b7653131895a1ceebf4d6159126920f1cf2c21ea4028ac5b731d1f","observation_id":"e6fc3f04-8dd1-4dfd-99fe-6496acddfc2f","resolution":{"observed_at":"2026-07-02T11:56:56.035264Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1305.4825","last_updated":"2016-09-17T09:42:53Z","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds","version":2},"cited_work":{"arxiv_id":"1305.4825","doi":null,"metadata_source":"pith","pith_arxiv_id":"1305.4825","snapshot_observed_at":"2026-07-09T02:35:53.832702Z","title":"Learning subgaussian classes : Upper and minimax bounds","venue":"math.ST","work_id":"e86711f0-1c7f-4d80-b638-bded5ad25f47","year":2013},"citing_paper":{"arxiv_id":"2607.07694","last_updated":"2026-07-08T17:50:15Z","snapshot_observed_at":"2026-08-15T13:37:25.888959Z","submitted_at":"2026-07-08T17:50:15Z","title":"Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity","version":1},"reference_index":270,"source":"arxiv_source","source_observed_at":"2026-07-09T02:31:10.557742Z"},"links":{"cited_paper":"/paper/1305.4825","citing_paper":"/paper/2607.07694"},"observation_digest":"sha256:16481a7841fff4f5a94c484ca663416c02751ba41c084967e043203017c38a15","observation_id":"42af68fe-1409-4c08-9e13-65a3f0992ed4","resolution":{"observed_at":"2026-07-09T02:35:53.833909Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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/1305.4825/citation-record","integrity":"/paper/1305.4825/integrity","json":"/paper/1305.4825/citation-record.json","paper":"/paper/1305.4825"},"outbound":[],"paper":{"arxiv_id":"1305.4825","last_updated":"2016-09-17T09:42:53Z","latest_version":2,"primary_category":"math.ST","snapshot_observed_at":"2026-08-15T00:17:08.643949Z","submitted_at":"2013-05-21T14:02:51Z","title":"Learning subgaussian classes : Upper and minimax bounds"},"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 6 inbound Pith citation observations for arXiv:1305.4825."}