{"as_of":"2026-08-09T02:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3e883969a97b08d3be470b7717d78eb3e1a29a0b073fd0272d06eb1f61b55575","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":"2026-08-08T06:32:00.761636+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":"2026-08-05T20:53:40.258611Z","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":"2026-05-11T08:56:02.615014Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.02233","last_updated":"2025-02-05T14:22:26Z","snapshot_observed_at":"2026-07-06T17:39:20.748463Z","submitted_at":"2024-03-04T17:24:03Z","title":"A Theoretical Analysis of Self-Supervised Learning for Vision Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02233","snapshot_observed_at":"2026-08-05T20:53:40.258611Z","title":"Cao, Y ., Chen, Z., Belkin, M., and Gu, Q","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.09824","last_updated":"2025-08-15T01:40:46Z","snapshot_observed_at":"2026-08-05T20:53:05.616467Z","submitted_at":"2025-08-13T13:56:01Z","title":"Reverse Convolution and Its Applications to Image Restoration","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T20:53:40.258611Z"},"links":{"cited_paper":"/paper/2403.02233","citing_paper":"/paper/2508.09824"},"observation_digest":"sha256:4e139c1aa4230665e4532fd7235970b24c6394588083920ba203c587e4c4f33c","observation_id":"9b8c1007-ece6-4e5f-bd28-1ffd1921598a","resolution":{"observed_at":"2026-08-05T20:53:40.258611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02233","last_updated":"2025-02-05T14:22:26Z","snapshot_observed_at":"2026-07-06T17:39:20.748463Z","submitted_at":"2024-03-04T17:24:03Z","title":"A Theoretical Analysis of Self-Supervised Learning for Vision Transformers","version":3},"cited_work":{"arxiv_id":"2403.02233","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.02233","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Transformers provably learn feature-position correlations in masked image modeling","venue":null,"work_id":"9c668160-b5a8-4c50-ad57-e75de072ae72","year":2024},"citing_paper":{"arxiv_id":"2604.10074","last_updated":"2026-04-11T07:46:15Z","snapshot_observed_at":"2026-07-06T22:58:47.599383Z","submitted_at":"2026-04-11T07:46:15Z","title":"Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-10T16:23:50.751356Z"},"links":{"cited_paper":"/paper/2403.02233","citing_paper":"/paper/2604.10074"},"observation_digest":"sha256:491ae3b3db608106c344e746d059b2b7ee518707f3427ddb13f94145e050d1c6","observation_id":"bb2c4937-1c70-4634-88f2-61ed35f35268","resolution":{"observed_at":"2026-05-11T08:56:02.627288Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.02233/citation-record","integrity":"/paper/2403.02233/integrity","json":"/paper/2403.02233/citation-record.json","paper":"/paper/2403.02233"},"outbound":[],"paper":{"arxiv_id":"2403.02233","last_updated":"2025-02-05T14:22:26Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:39:20.748463Z","submitted_at":"2024-03-04T17:24:03Z","title":"A Theoretical Analysis of Self-Supervised Learning for Vision Transformers"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.02233."}