{"as_of":"2026-08-16T09:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32cba73afd222a67d670dded07cd03fb8a1e6f4575fe9b8b04975c0340d310e4","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T15:05:19.356578Z","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":"2026-05-09T23:04:17.764753Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.09059","last_updated":"2022-11-24T21:40:45Z","snapshot_observed_at":"2026-08-15T13:18:49.633322Z","submitted_at":"2022-06-18T00:16:37Z","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09059","snapshot_observed_at":"2026-08-11T17:15:25.498363Z","title":"CLiMB: A Continual Learn- ing Benchmark for Vision-and-Language Tasks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09240","last_updated":"2024-12-12T12:49:42Z","snapshot_observed_at":"2026-08-15T02:40:25.306393Z","submitted_at":"2024-12-12T12:49:42Z","title":"VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T17:15:25.498363Z"},"links":{"cited_paper":"/paper/2206.09059","citing_paper":"/paper/2412.09240"},"observation_digest":"sha256:db760f750dcb1ea1cb1509835b5a0336c387099269134a4cb24055936770f0b2","observation_id":"486f52ff-4091-41e2-878a-ec25699459c6","resolution":{"observed_at":"2026-08-11T17:15:25.498363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09059","last_updated":"2022-11-24T21:40:45Z","snapshot_observed_at":"2026-08-15T13:18:49.633322Z","submitted_at":"2022-06-18T00:16:37Z","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks","version":2},"cited_work":{"arxiv_id":"2206.09059","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09059","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tejas Srinivasan, Ting-Yun Chang, Leticia Pinto-Alva, Georgios Chochlakis, Mohammad Rostami, and Jesse Thomason","venue":null,"work_id":"dd008128-19b9-4959-94b5-bdb45504638d","year":null},"citing_paper":{"arxiv_id":"2604.21927","last_updated":"2026-07-07T13:25:47Z","snapshot_observed_at":"2026-08-08T00:21:51.556773Z","submitted_at":"2026-04-23T17:59:34Z","title":"Fine-Tuning Regimes Define Distinct Continual Learning Problems","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-09T23:02:19.128635Z"},"links":{"cited_paper":"/paper/2206.09059","citing_paper":"/paper/2604.21927"},"observation_digest":"sha256:024e06df56c1553899d38f099cba6c78304541280a15931df210586ca461cda1","observation_id":"3acf02f4-486d-44f6-a157-47a09e376fa0","resolution":{"observed_at":"2026-05-09T23:04:17.766423Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09059","last_updated":"2022-11-24T21:40:45Z","snapshot_observed_at":"2026-08-15T13:18:49.633322Z","submitted_at":"2022-06-18T00:16:37Z","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09059","snapshot_observed_at":"2026-07-12T18:36:06.754809Z","title":"Tejas Srinivasan, Ting-Yun Chang, Leticia Pinto-Alva, Georgios Chochlakis, Mohammad Rostami, and Jesse Thomason","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.21927","last_updated":"2026-07-07T13:25:47Z","snapshot_observed_at":"2026-08-08T00:21:51.556773Z","submitted_at":"2026-04-23T17:59:34Z","title":"Fine-Tuning Regimes Define Distinct Continual Learning Problems","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-12T18:36:06.754809Z"},"links":{"cited_paper":"/paper/2206.09059","citing_paper":"/paper/2604.21927"},"observation_digest":"sha256:c637b01602c56c64140436ad0c4e971bd3c946f68ce7001f988137405d28ba42","observation_id":"e9689b9e-b230-42d9-9ec0-622dc3ec5a0d","resolution":{"observed_at":"2026-07-12T18:36:06.754809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09059","last_updated":"2022-11-24T21:40:45Z","snapshot_observed_at":"2026-08-15T13:18:49.633322Z","submitted_at":"2022-06-18T00:16:37Z","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09059","snapshot_observed_at":"2026-08-15T15:05:19.356578Z","title":"In: Advances in Neural Information Processing Systems, pp 29440–29453, URL https://arxiv.org/abs/2206.09059","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.02830","last_updated":"2026-08-03T19:44:45Z","snapshot_observed_at":"2026-08-15T17:05:29.091571Z","submitted_at":"2026-08-03T19:44:45Z","title":"In-Context Collapse in Vision-Language Models and How to Mitigate it?","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T15:05:19.356578Z"},"links":{"cited_paper":"/paper/2206.09059","citing_paper":"/paper/2608.02830"},"observation_digest":"sha256:7c52367293f3f9674ab193b0baab7098794aca34eb1c6dd012efc3a2e0b181ad","observation_id":"724ccdc1-0fc3-48d2-ade9-819970cae487","resolution":{"observed_at":"2026-08-15T15:05:19.356578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2206.09059/citation-record","integrity":"/paper/2206.09059/integrity","json":"/paper/2206.09059/citation-record.json","paper":"/paper/2206.09059"},"outbound":[],"paper":{"arxiv_id":"2206.09059","last_updated":"2022-11-24T21:40:45Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T13:18:49.633322Z","submitted_at":"2022-06-18T00:16:37Z","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2206.09059."}