{"as_of":"2026-08-05T15:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eeb854da86d5be5f7746b66b9482578cb686bd2020db19d5dc30b05a8e1cf21e","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T13:31:55.497762Z","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":"2026-07-03T04:57:38.406925Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.04841","last_updated":"2024-02-07T13:41:53Z","snapshot_observed_at":"2026-07-31T19:55:48.320963Z","submitted_at":"2024-02-07T13:41:53Z","title":"Data-efficient Large Vision Models through Sequential Autoregression","version":1},"cited_work":{"arxiv_id":"2402.04841","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04841","snapshot_observed_at":"2026-07-03T04:57:38.406925Z","title":"arXiv preprint arXiv:2402.04841 (2024)","venue":null,"work_id":"ebfd5ce0-6c64-4e71-813b-bcabcf1a566f","year":2024},"citing_paper":{"arxiv_id":"2604.05651","last_updated":"2026-04-07T09:54:37Z","snapshot_observed_at":"2026-07-06T22:54:21.571606Z","submitted_at":"2026-04-07T09:54:37Z","title":"Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T19:54:48.926387Z"},"links":{"cited_paper":"/paper/2402.04841","citing_paper":"/paper/2604.05651"},"observation_digest":"sha256:a402aa46a430a4f58fea79d50b11af84277e818e5dc6ab92d12f8411b23fa5d2","observation_id":"70f50eae-745f-4107-bb13-416c2d31ca6e","resolution":{"observed_at":"2026-05-10T22:25:50.103898Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04841","last_updated":"2024-02-07T13:41:53Z","snapshot_observed_at":"2026-07-31T19:55:48.320963Z","submitted_at":"2024-02-07T13:41:53Z","title":"Data-efficient Large Vision Models through Sequential Autoregression","version":1},"cited_work":{"arxiv_id":"2402.04841","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04841","snapshot_observed_at":"2026-07-03T04:57:38.406925Z","title":"arXiv preprint arXiv:2402.04841 (2024)","venue":null,"work_id":"ebfd5ce0-6c64-4e71-813b-bcabcf1a566f","year":2024},"citing_paper":{"arxiv_id":"2604.06748","last_updated":"2026-04-08T07:13:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-08T07:13:56Z","title":"From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T18:50:08.354787Z"},"links":{"cited_paper":"/paper/2402.04841","citing_paper":"/paper/2604.06748"},"observation_digest":"sha256:778112e351f1b396fb483da09c78d7699f52276cd08c690d7f173555f61d8901","observation_id":"8f85c82a-1627-4218-b997-3508cf5b8f9b","resolution":{"observed_at":"2026-05-10T23:50:56.324977Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04841","last_updated":"2024-02-07T13:41:53Z","snapshot_observed_at":"2026-07-31T19:55:48.320963Z","submitted_at":"2024-02-07T13:41:53Z","title":"Data-efficient Large Vision Models through Sequential Autoregression","version":1},"cited_work":{"arxiv_id":"2402.04841","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04841","snapshot_observed_at":"2026-07-03T04:57:38.406925Z","title":"arXiv preprint arXiv:2402.04841 (2024)","venue":null,"work_id":"ebfd5ce0-6c64-4e71-813b-bcabcf1a566f","year":2024},"citing_paper":{"arxiv_id":"2606.10905","last_updated":"2026-06-09T14:13:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-09T14:13:58Z","title":"Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T13:31:55.497762Z"},"links":{"cited_paper":"/paper/2402.04841","citing_paper":"/paper/2606.10905"},"observation_digest":"sha256:4c686ab723d13e88db59d710e60bbf6bb818089f2255a0ff84f5205cb6a1ad76","observation_id":"95662100-cdc7-4167-a714-c6f5d98b59b1","resolution":{"observed_at":"2026-07-03T04:57:38.408241Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.04841/citation-record","integrity":"/paper/2402.04841/integrity","json":"/paper/2402.04841/citation-record.json","paper":"/paper/2402.04841"},"outbound":[],"paper":{"arxiv_id":"2402.04841","last_updated":"2024-02-07T13:41:53Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-31T19:55:48.320963Z","submitted_at":"2024-02-07T13:41:53Z","title":"Data-efficient Large Vision Models through Sequential Autoregression"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.04841."}