{"as_of":"2026-08-07T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9b17f7a2d14611bbc6f79a50c7654a715a839a6532df7527f5efe693d2907d2f","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":"2026-08-07T06:34:17.273281+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":"2026-08-02T16:42:42.870073Z","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":"2026-05-10T23:50:53.755219Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.10502","last_updated":"2024-06-15T04:50:20Z","snapshot_observed_at":"2026-07-06T18:31:21.929350Z","submitted_at":"2024-06-15T04:50:20Z","title":"Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data","version":1},"cited_work":{"arxiv_id":"2406.10502","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10502","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2406.10502 (2024) 3","venue":null,"work_id":"900a189b-3ad8-4eba-ae3e-836832053646","year":2024},"citing_paper":{"arxiv_id":"2604.06614","last_updated":"2026-07-15T10:29:40Z","snapshot_observed_at":"2026-08-02T16:42:37.460383Z","submitted_at":"2026-04-08T02:49:19Z","title":"Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T18:51:00.869720Z"},"links":{"cited_paper":"/paper/2406.10502","citing_paper":"/paper/2604.06614"},"observation_digest":"sha256:7b2cda36f07658de8aee07d7bf25a528f87d9435a29c7a62d3256e754b4dc784","observation_id":"ec955c4c-f3f0-4eaa-8765-52245f28e534","resolution":{"observed_at":"2026-05-10T23:50:53.758166Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10502","last_updated":"2024-06-15T04:50:20Z","snapshot_observed_at":"2026-07-06T18:31:21.929350Z","submitted_at":"2024-06-15T04:50:20Z","title":"Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10502","snapshot_observed_at":"2026-08-02T16:42:42.870073Z","title":"arXiv preprint arXiv:2406.10502 (2024) 3","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.06614","last_updated":"2026-07-15T10:29:40Z","snapshot_observed_at":"2026-08-02T16:42:37.460383Z","submitted_at":"2026-04-08T02:49:19Z","title":"Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T16:42:42.870073Z"},"links":{"cited_paper":"/paper/2406.10502","citing_paper":"/paper/2604.06614"},"observation_digest":"sha256:515db97519bf0570cbe644ceb54bed7a3b1b86f523354b47942eab1a2d3f4ff3","observation_id":"ad59e40e-ef10-48db-b470-dbd4729eb0a3","resolution":{"observed_at":"2026-08-02T16:42:42.870073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.10502/citation-record","integrity":"/paper/2406.10502/integrity","json":"/paper/2406.10502/citation-record.json","paper":"/paper/2406.10502"},"outbound":[],"paper":{"arxiv_id":"2406.10502","last_updated":"2024-06-15T04:50:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:31:21.929350Z","submitted_at":"2024-06-15T04:50:20Z","title":"Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.10502."}