{"as_of":"2026-08-06T13:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f437effb104e0fee0f72f7deb37ee43d8863901544a0a4f64bc0bd61cd4250d","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T20:06:44.480769Z","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":"2026-05-13T20:06:44.633397Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.00990","last_updated":"2022-10-03T14:56:05Z","snapshot_observed_at":"2026-07-06T13:59:04.148900Z","submitted_at":"2022-10-03T14:56:05Z","title":"Visual Prompt Tuning for Generative Transfer Learning","version":1},"cited_work":{"arxiv_id":"2210.00990","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.00990","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv:2210.00990 , year=","venue":null,"work_id":"1092d47f-9a2c-429f-86f1-404bd4b713ad","year":null},"citing_paper":{"arxiv_id":"2310.05737","last_updated":"2024-03-29T17:44:41Z","snapshot_observed_at":"2026-08-02T18:23:02.746177Z","submitted_at":"2023-10-09T14:10:29Z","title":"Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation","version":3},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-05-13T20:06:44.480769Z"},"links":{"cited_paper":"/paper/2210.00990","citing_paper":"/paper/2310.05737"},"observation_digest":"sha256:df98365f33825d506559ca5444ff5d629ce2cc7fd44407d9f8081f36c56e435c","observation_id":"c2aa6aa5-0c4f-4451-a527-072689507745","resolution":{"observed_at":"2026-05-13T20:06:44.635330Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2210.00990/citation-record","integrity":"/paper/2210.00990/integrity","json":"/paper/2210.00990/citation-record.json","paper":"/paper/2210.00990"},"outbound":[],"paper":{"arxiv_id":"2210.00990","last_updated":"2022-10-03T14:56:05Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T13:59:04.148900Z","submitted_at":"2022-10-03T14:56:05Z","title":"Visual Prompt Tuning for Generative Transfer 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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2210.00990."}