{"as_of":"2026-08-18T16:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:96858c60d99293f4527f8f1f6d9c4deb1fb4c4f1b12d9f87e50d0a6a99ed84c9","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":"2026-08-15T22:39:46.245652Z","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":"2026-05-13T22:46:10.106726Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.13496","last_updated":"2024-01-25T03:20:12Z","snapshot_observed_at":"2026-08-17T00:03:54.282427Z","submitted_at":"2023-03-23T17:56:12Z","title":"The effectiveness of MAE pre-pretraining for billion-scale pretraining","version":3},"cited_work":{"arxiv_id":"2303.13496","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.13496","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The effectiveness of mae pre-pretraining for billion-scale pretraining","venue":null,"work_id":"5604f679-f6be-4cb1-8ce3-c1ea98fdcacc","year":null},"citing_paper":{"arxiv_id":"2312.14238","last_updated":"2024-01-15T15:23:55Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-21T18:59:31Z","title":"InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks","version":3},"reference_index":129,"source":"pdf_text","source_observed_at":"2026-05-13T22:46:09.693156Z"},"links":{"cited_paper":"/paper/2303.13496","citing_paper":"/paper/2312.14238"},"observation_digest":"sha256:9cd0cead8bfac4a7210268fb4948bd7447a37296b48cd35bc982f44e57b56204","observation_id":"cdd5b176-3708-454b-8ce4-402be697162a","resolution":{"observed_at":"2026-05-13T22:46:10.109386Z","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":"2303.13496","last_updated":"2024-01-25T03:20:12Z","snapshot_observed_at":"2026-08-17T00:03:54.282427Z","submitted_at":"2023-03-23T17:56:12Z","title":"The effectiveness of MAE pre-pretraining for billion-scale pretraining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.13496","snapshot_observed_at":"2026-08-15T22:39:46.245652Z","title":"Singh, Q","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.06710","last_updated":"2025-05-10T17:23:36Z","snapshot_observed_at":"2026-08-18T10:43:46.482251Z","submitted_at":"2025-05-10T17:23:36Z","title":"SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:46.245652Z"},"links":{"cited_paper":"/paper/2303.13496","citing_paper":"/paper/2505.06710"},"observation_digest":"sha256:8c49c302e59063309db6752e11e9653a870d2f3ce572a16d406011d9efbfb092","observation_id":"92706e5b-b3d8-46d6-a611-b08cdcea6f0f","resolution":{"observed_at":"2026-08-15T22:39:46.245652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2303.13496/citation-record","integrity":"/paper/2303.13496/integrity","json":"/paper/2303.13496/citation-record.json","paper":"/paper/2303.13496"},"outbound":[],"paper":{"arxiv_id":"2303.13496","last_updated":"2024-01-25T03:20:12Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T00:03:54.282427Z","submitted_at":"2023-03-23T17:56:12Z","title":"The effectiveness of MAE pre-pretraining for billion-scale pretraining"},"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 2 inbound Pith citation observations for arXiv:2303.13496."}