{"as_of":"2026-08-08T01:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c035af4afacb03d2a417baf4218aa1fac0016d5fa697e63dff85253064e9482a","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T13:15:37.660314Z","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":"2026-07-10T08:36:59.875751Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":"2109.02355","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-07-10T08:36:59.875751Z","title":"A Fa rewell to the Bias-Variance Tradeoﬀ? An Overview of the Theory of Overparameterized Machine Learning","venue":"stat.ML","work_id":"5ff39e0d-48e4-4a16-a73e-95b4ad91a32a","year":2021},"citing_paper":{"arxiv_id":"2305.02304","last_updated":"2023-05-03T17:52:40Z","snapshot_observed_at":"2026-08-02T06:41:32.067203Z","submitted_at":"2023-05-03T17:52:40Z","title":"New Equivalences Between Interpolation and SVMs: Kernels and Structured Features","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T08:49:14.370358Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2305.02304"},"observation_digest":"sha256:6729ca2c85cedc1bd5a0483ed70fe250e8ac647d660c09926b65577b9acdaf36","observation_id":"136b2188-dc51-4921-a7e2-53f62374f429","resolution":{"observed_at":"2026-05-24T08:54:15.985048Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-08-07T13:15:37.660314Z","title":"A farewell to the bias-variance tradeoff? an overview of the theory of overparameterized machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22355","last_updated":"2025-05-28T13:35:12Z","snapshot_observed_at":"2026-08-07T20:47:52.277664Z","submitted_at":"2025-05-28T13:35:12Z","title":"Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:37.660314Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2505.22355"},"observation_digest":"sha256:06ec19f7c82b0f61af8520f063b631089b0be5c28525d67c04420cee14641b63","observation_id":"1377627b-25c8-402b-a37b-5835adf133fa","resolution":{"observed_at":"2026-08-07T13:15:37.660314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":"2109.02355","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-07-10T08:36:59.875751Z","title":"A Fa rewell to the Bias-Variance Tradeoﬀ? An Overview of the Theory of Overparameterized Machine Learning","venue":"stat.ML","work_id":"5ff39e0d-48e4-4a16-a73e-95b4ad91a32a","year":2021},"citing_paper":{"arxiv_id":"2605.21494","last_updated":"2026-04-15T16:31:23Z","snapshot_observed_at":"2026-08-02T13:50:05.299449Z","submitted_at":"2026-04-15T16:31:23Z","title":"Double descent for least-squares interpolation on contaminated data: A simulation study","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-22T01:23:41.854790Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2605.21494"},"observation_digest":"sha256:4e7f9e89c70e7db4495bc32baf1991987b8a029e865074e7915926c798cac0ca","observation_id":"85a09755-08a0-4d62-bdf5-eb4794ab0ff8","resolution":{"observed_at":"2026-05-22T01:24:30.308773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-07-12T01:11:50.444218Z","title":"arXiv preprint arXiv:2109.02355 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03613","last_updated":"2026-07-03T22:03:13Z","snapshot_observed_at":"2026-07-12T01:11:45.525555Z","submitted_at":"2026-07-03T22:03:13Z","title":"Implicit Bias of SGD in Multivariate ReLU Networks: Effective Width Collapse","version":1},"reference_index":216,"source":"arxiv_source","source_observed_at":"2026-07-12T01:11:50.444218Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2607.03613"},"observation_digest":"sha256:884a9df03d25a2c4b3e1b358d190eb4d9d979ddd9982783d6857a7aaff671770","observation_id":"c0243da7-274b-4f2e-93c6-5ce6df4e96d5","resolution":{"observed_at":"2026-07-12T01:11:50.444218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":"2109.02355","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-07-10T08:36:59.875751Z","title":"A Fa rewell to the Bias-Variance Tradeoﬀ? An Overview of the Theory of Overparameterized Machine Learning","venue":"stat.ML","work_id":"5ff39e0d-48e4-4a16-a73e-95b4ad91a32a","year":2021},"citing_paper":{"arxiv_id":"2607.08377","last_updated":"2026-07-09T11:54:53Z","snapshot_observed_at":"2026-08-05T23:45:20.014471Z","submitted_at":"2026-07-09T11:54:53Z","title":"Eigenvalue Calibration for Semantic Embeddings of Large Language Models","version":1},"reference_index":231,"source":"arxiv_source","source_observed_at":"2026-07-10T08:27:17.038307Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2607.08377"},"observation_digest":"sha256:d9e44087dc1c97eeb0f02b8218130428bdfffb0c2df44f5104fa307803453102","observation_id":"57436a2a-5f7e-4bb3-9813-9039ef5ce396","resolution":{"observed_at":"2026-07-10T08:36:59.877084Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-08-01T07:39:34.243060Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21409","last_updated":"2026-08-05T14:32:28Z","snapshot_observed_at":"2026-08-08T01:12:44.996805Z","submitted_at":"2026-07-23T15:11:31Z","title":"Cautious optimism for deep parameterized quantum circuits","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T07:39:34.243060Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2607.21409"},"observation_digest":"sha256:65e8db35e6f2e0510c8efbaae545aa1a58e90a8ed25d57e65a900e73da03ad63","observation_id":"83ce8162-533f-4db4-b0d6-15931278954e","resolution":{"observed_at":"2026-08-01T07:39:34.243060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02355","snapshot_observed_at":"2026-08-06T00:43:09.319426Z","title":"Baraniuk","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.01032","last_updated":"2026-08-02T06:27:55Z","snapshot_observed_at":"2026-08-07T21:11:29.897730Z","submitted_at":"2026-08-02T06:27:55Z","title":"The Fourth Quadrant: A Stylized View of Benign Misfitting","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T00:43:09.319426Z"},"links":{"cited_paper":"/paper/2109.02355","citing_paper":"/paper/2608.01032"},"observation_digest":"sha256:8e9de1dd7161fb89ccc00eaed44fdeee3cdb34bb19166eeaea447c4a36934e04","observation_id":"7a2c4d18-6c15-4b1e-b879-3387d78cf3fd","resolution":{"observed_at":"2026-08-06T00:43:09.319426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2109.02355/citation-record","integrity":"/paper/2109.02355/integrity","json":"/paper/2109.02355/citation-record.json","paper":"/paper/2109.02355"},"outbound":[],"paper":{"arxiv_id":"2109.02355","last_updated":"2021-09-06T10:48:40Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T11:44:36.471158Z","submitted_at":"2021-09-06T10:48:40Z","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2109.02355."}