{"as_of":"2026-08-19T03:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed4383a91b9bf3dad2ac242056774a2e325ae0ffa2ec2a3cf6d33f3bf3ecaecf","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:09:35.271048Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-10T05:30:23.456663Z","state":"measured"}],"external_citation_measurements":[{"count":5,"observed_at":"2026-08-10T05:30:23.456663Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.01717","last_updated":"2022-06-03T17:49:38Z","snapshot_observed_at":"2026-08-18T13:36:06.190284Z","submitted_at":"2022-06-03T17:49:38Z","title":"A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01717","snapshot_observed_at":"2026-08-11T20:09:35.271048Z","title":"A theoretical analysis on feature learning in neural networks: Emergence from inputs and advantage over fixed features","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06061","last_updated":"2025-02-28T20:36:37Z","snapshot_observed_at":"2026-08-16T17:04:35.684321Z","submitted_at":"2024-12-08T20:29:06Z","title":"Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond","version":2},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-08-11T20:09:35.271048Z"},"links":{"cited_paper":"/paper/2206.01717","citing_paper":"/paper/2412.06061"},"observation_digest":"sha256:57cb09ae6b3b23e7f343c6ae0955340783a828a005c74af7ae051bb20cf7939e","observation_id":"fa6a2f72-b47b-4e36-b34e-5b8fd04ff14b","resolution":{"observed_at":"2026-08-11T20:09:35.271048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01717","last_updated":"2022-06-03T17:49:38Z","snapshot_observed_at":"2026-08-18T13:36:06.190284Z","submitted_at":"2022-06-03T17:49:38Z","title":"A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features","version":1},"cited_work":{"arxiv_id":"2206.01717","doi":"10.48550/arxiv.2206.01717","metadata_source":"pith","pith_arxiv_id":"2206.01717","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features","venue":"cs.LG","work_id":"92330ae1-4d9e-41d3-9865-f54e47f790c5","year":2022},"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":58,"source":"arxiv_source","source_observed_at":"2026-08-06T14:17:37.208534Z"},"links":{"cited_paper":"/paper/2206.01717","citing_paper":"/paper/2507.19680"},"observation_digest":"sha256:dd88679f3ee02a66f368d3b1ef25ca9203145e7e7f065bf4421daf1ea75909a7","observation_id":"fb5dc8c5-2d2a-4888-aad6-f1915755851c","resolution":{"observed_at":"2026-08-06T14:17:37.553585Z","resolver_source":"local_arxiv","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/2206.01717/citation-record","integrity":"/paper/2206.01717/integrity","json":"/paper/2206.01717/citation-record.json","paper":"/paper/2206.01717"},"outbound":[],"paper":{"arxiv_id":"2206.01717","last_updated":"2022-06-03T17:49:38Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T13:36:06.190284Z","submitted_at":"2022-06-03T17:49:38Z","title":"A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features"},"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 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2206.01717."}