{"as_of":"2026-08-14T13:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:df3f6a8bf113daf27fb50ec0abec6c6dd4713ed765285127a4e2259718fb855f","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:17:39.791309Z","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":"2026-05-17T03:27:59.050732Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.11250","last_updated":"2021-06-21T16:48:19Z","snapshot_observed_at":"2026-08-13T18:59:43.114751Z","submitted_at":"2021-06-21T16:48:19Z","title":"VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning","version":1},"cited_work":{"arxiv_id":"2106.11250","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.11250","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2106.11250 , year=","venue":null,"work_id":"027ef24d-32dc-4811-acb5-fbe62daaf143","year":2022},"citing_paper":{"arxiv_id":"2111.07832","last_updated":"2022-01-27T09:20:49Z","snapshot_observed_at":"2026-07-06T12:08:39.149450Z","submitted_at":"2021-11-15T15:18:05Z","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-14T02:10:27.569787Z"},"links":{"cited_paper":"/paper/2106.11250","citing_paper":"/paper/2111.07832"},"observation_digest":"sha256:7835829ef0d422b7c805c6a62263eda012c90d4b3aa8e989dfd5ef4c4b850037","observation_id":"2a4974bd-720c-4f37-a802-a66669be2dcf","resolution":{"observed_at":"2026-05-14T02:10:27.612571Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11250","last_updated":"2021-06-21T16:48:19Z","snapshot_observed_at":"2026-08-13T18:59:43.114751Z","submitted_at":"2021-06-21T16:48:19Z","title":"VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning","version":1},"cited_work":{"arxiv_id":"2106.11250","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.11250","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2106.11250 , year=","venue":null,"work_id":"027ef24d-32dc-4811-acb5-fbe62daaf143","year":2022},"citing_paper":{"arxiv_id":"2309.16671","last_updated":"2025-11-23T00:34:43Z","snapshot_observed_at":"2026-08-02T18:24:11.208164Z","submitted_at":"2023-09-28T17:59:56Z","title":"Demystifying CLIP Data","version":6},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-16T09:20:20.143143Z"},"links":{"cited_paper":"/paper/2106.11250","citing_paper":"/paper/2309.16671"},"observation_digest":"sha256:4a022cad3c1eae0f4a0d2b14f993eeb577da1ff8980311551b650acd98c346a3","observation_id":"b1843a3a-2cd7-422a-8e86-1e735ce3917d","resolution":{"observed_at":"2026-05-16T09:20:20.337260Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11250","last_updated":"2021-06-21T16:48:19Z","snapshot_observed_at":"2026-08-13T18:59:43.114751Z","submitted_at":"2021-06-21T16:48:19Z","title":"VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning","version":1},"cited_work":{"arxiv_id":"2106.11250","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.11250","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2106.11250 , year=","venue":null,"work_id":"027ef24d-32dc-4811-acb5-fbe62daaf143","year":2022},"citing_paper":{"arxiv_id":"2310.01852","last_updated":"2024-01-22T03:11:15Z","snapshot_observed_at":"2026-08-14T03:13:25.123360Z","submitted_at":"2023-10-03T07:33:27Z","title":"LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment","version":7},"reference_index":192,"source":"arxiv_source","source_observed_at":"2026-05-17T03:27:58.952076Z"},"links":{"cited_paper":"/paper/2106.11250","citing_paper":"/paper/2310.01852"},"observation_digest":"sha256:667b1bd7da947cfb0de78eb061e6cf05edb30f15583018a315e2ccea76cbb7a3","observation_id":"6879fc0d-6e60-4db5-870b-7b75471a1633","resolution":{"observed_at":"2026-05-17T03:27:59.053428Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11250","last_updated":"2021-06-21T16:48:19Z","snapshot_observed_at":"2026-08-13T18:59:43.114751Z","submitted_at":"2021-06-21T16:48:19Z","title":"VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning","version":1},"cited_work":{"arxiv_id":"2106.11250","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.11250","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2106.11250 , year=","venue":null,"work_id":"027ef24d-32dc-4811-acb5-fbe62daaf143","year":2022},"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":38,"source":"arxiv_source","source_observed_at":"2026-05-13T20:06:44.480769Z"},"links":{"cited_paper":"/paper/2106.11250","citing_paper":"/paper/2310.05737"},"observation_digest":"sha256:68a29979d3a40a433fc71ac6f81a5363404968c7011e3ed163787259189e5e90","observation_id":"f829d4aa-d66d-47f4-a23b-9b54d7ec30bc","resolution":{"observed_at":"2026-05-13T20:06:44.647062Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11250","last_updated":"2021-06-21T16:48:19Z","snapshot_observed_at":"2026-08-13T18:59:43.114751Z","submitted_at":"2021-06-21T16:48:19Z","title":"VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.11250","snapshot_observed_at":"2026-08-08T19:17:39.791309Z","title":"Vimpac: Video pre-training via masked token prediction and con- trastive learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07811","last_updated":"2025-02-08T06:15:39Z","snapshot_observed_at":"2026-08-14T11:37:12.426263Z","submitted_at":"2025-02-08T06:15:39Z","title":"CrossVideoMAE: Self-Supervised Image-Video Representation Learning with Masked Autoencoders","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-08T19:17:39.791309Z"},"links":{"cited_paper":"/paper/2106.11250","citing_paper":"/paper/2502.07811"},"observation_digest":"sha256:5f2c1b77f5f1912c7f45761cb9e9d416f245bfaf05de5b5b965de4f2f9721c23","observation_id":"090a7cd6-6cc6-48d3-9c96-edda12c8ba7e","resolution":{"observed_at":"2026-08-08T19:17:39.791309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.11250/citation-record","integrity":"/paper/2106.11250/integrity","json":"/paper/2106.11250/citation-record.json","paper":"/paper/2106.11250"},"outbound":[],"paper":{"arxiv_id":"2106.11250","last_updated":"2021-06-21T16:48:19Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T18:59:43.114751Z","submitted_at":"2021-06-21T16:48:19Z","title":"VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive 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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2106.11250."}