{"as_of":"2026-08-18T17:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:196e19b2b392fc8fec9b28aa1fee94591530ce84849a03d2a1d85058dbe4229e","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":"2026-08-18T06:34:40.430872+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":"2026-08-12T12:32:47.894602Z","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":"2026-07-03T00:47:30.986892Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","version":6},"cited_work":{"arxiv_id":"2001.04413","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.04413","snapshot_observed_at":"2026-07-03T00:47:30.986892Z","title":"arXiv preprint arXiv:2001.04413 , year=","venue":null,"work_id":"96e52f68-dd80-4294-811e-2ca36ff306e9","year":2001},"citing_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-17T18:04:53.578114Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-09T05:01:39.906340Z"},"links":{"cited_paper":"/paper/2001.04413","citing_paper":"/paper/2106.09685"},"observation_digest":"sha256:a097740eb0bab884ebac453e58b1cb38bacb216da456014658bcdbf08146ae9c","observation_id":"9869e8cf-08ff-49f0-9083-e59ec35fd062","resolution":{"observed_at":"2026-05-09T05:01:40.873685Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","version":6},"cited_work":{"arxiv_id":"2001.04413","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.04413","snapshot_observed_at":"2026-07-03T00:47:30.986892Z","title":"arXiv preprint arXiv:2001.04413 , year=","venue":null,"work_id":"96e52f68-dd80-4294-811e-2ca36ff306e9","year":2001},"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":242,"source":"arxiv_source","source_observed_at":"2026-05-14T23:00:20.720030Z"},"links":{"cited_paper":"/paper/2001.04413","citing_paper":"/paper/2401.01335"},"observation_digest":"sha256:8fa8c576e0bbf7e6f007f65b73e1195ea3b0619918a53068ca612f158de6f983","observation_id":"80df03b2-e6b1-4755-bfc3-850363f2ebe9","resolution":{"observed_at":"2026-05-14T23:00:21.082572Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04413","snapshot_observed_at":"2026-08-12T12:32:47.894602Z","title":"Backward feature correction: How deep learning performs deep learning","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.17201","last_updated":"2024-11-26T08:14:48Z","snapshot_observed_at":"2026-08-12T12:21:30.416946Z","submitted_at":"2024-11-26T08:14:48Z","title":"Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-12T12:32:47.894602Z"},"links":{"cited_paper":"/paper/2001.04413","citing_paper":"/paper/2411.17201"},"observation_digest":"sha256:94175b1fc5264da60363703046ea7b298499cc8bc2801fbf3f464d7137539bf1","observation_id":"b6974251-0e80-4e69-86e6-d0990ae2470d","resolution":{"observed_at":"2026-08-12T12:32:47.894602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04413","snapshot_observed_at":"2026-08-06T14:17:37.019292Z","title":"Backward feature correction: How deep learning performs deep (hierarchical) learning","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.19680","last_updated":"2025-07-25T21:19:37Z","snapshot_observed_at":"2026-08-16T06:57:12.804004Z","submitted_at":"2025-07-25T21:19:37Z","title":"Feature learning is decoupled from generalization in high capacity neural networks","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T14:17:37.019292Z"},"links":{"cited_paper":"/paper/2001.04413","citing_paper":"/paper/2507.19680"},"observation_digest":"sha256:40ad3a1f1838ad60aaeab2dae18c54ecab328cf35050a117027022f8e0bd8394","observation_id":"c29254af-3f9c-49ba-90a1-6d36a13844e5","resolution":{"observed_at":"2026-08-06T14:17:37.019292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04413","snapshot_observed_at":"2026-08-03T15:22:46.274116Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17351","last_updated":"2026-07-28T16:53:50Z","snapshot_observed_at":"2026-08-17T09:18:32.359725Z","submitted_at":"2025-12-19T08:47:28Z","title":"Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T15:22:46.274116Z"},"links":{"cited_paper":"/paper/2001.04413","citing_paper":"/paper/2512.17351"},"observation_digest":"sha256:d26e8d5358054376fa85944baf32fd808a7174aecd6cc2a9b7b55bd0f56ef67d","observation_id":"38474fee-41a5-4f5e-be58-d9b179a651cf","resolution":{"observed_at":"2026-08-03T15:22:46.274116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning","version":6},"cited_work":{"arxiv_id":"2001.04413","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.04413","snapshot_observed_at":"2026-07-03T00:47:30.986892Z","title":"arXiv preprint arXiv:2001.04413 , year=","venue":null,"work_id":"96e52f68-dd80-4294-811e-2ca36ff306e9","year":2001},"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":261,"source":"arxiv_source","source_observed_at":"2026-06-27T16:59:34.084575Z"},"links":{"cited_paper":"/paper/2001.04413","citing_paper":"/paper/2606.10089"},"observation_digest":"sha256:c38c40736aa58d69674cec7b13c9f6173cbc5625f5bef0e979ef26ccf76875ef","observation_id":"bd969312-dc84-46be-8882-60223dec9b9f","resolution":{"observed_at":"2026-07-03T00:47:30.988373Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2001.04413/citation-record","integrity":"/paper/2001.04413/integrity","json":"/paper/2001.04413/citation-record.json","paper":"/paper/2001.04413"},"outbound":[],"paper":{"arxiv_id":"2001.04413","last_updated":"2023-07-07T06:12:32Z","latest_version":6,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T04:20:38.703555Z","submitted_at":"2020-01-13T17:28:29Z","title":"Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) 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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2001.04413."}