{"as_of":"2026-08-08T00:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2cbae1628de611153056cc9e141855b6ccb1161d0efa7da146f31884048f7809","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":"2026-08-06T23:28:15.348279Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T21:17:16.309288Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.12973","last_updated":"2024-05-06T15:45:30Z","snapshot_observed_at":"2026-08-07T22:14:28.327485Z","submitted_at":"2023-10-19T17:59:05Z","title":"Frozen Transformers in Language Models Are Effective Visual Encoder Layers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.12973","snapshot_observed_at":"2026-08-06T23:28:15.348279Z","title":"arXiv preprint arXiv:2310.12973 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18034","last_updated":"2025-06-22T13:34:00Z","snapshot_observed_at":"2026-08-07T15:27:20.029119Z","submitted_at":"2025-06-22T13:34:00Z","title":"Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:28:15.348279Z"},"links":{"cited_paper":"/paper/2310.12973","citing_paper":"/paper/2506.18034"},"observation_digest":"sha256:38134413a8c1b46ea7c52f988c54fcec42f27d57555df5f301b88ac32ec0aa91","observation_id":"39aa0589-0cce-45bf-95ad-a2044ceaac7c","resolution":{"observed_at":"2026-08-06T23:28:15.348279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12973","last_updated":"2024-05-06T15:45:30Z","snapshot_observed_at":"2026-08-07T22:14:28.327485Z","submitted_at":"2023-10-19T17:59:05Z","title":"Frozen Transformers in Language Models Are Effective Visual Encoder Layers","version":2},"cited_work":{"arxiv_id":"2310.12973","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.12973","snapshot_observed_at":"2026-08-06T21:17:16.309288Z","title":"Frozen Transformers in Language Models Are Effective Visual Encoder Layers","venue":"cs.CV","work_id":"9a576f92-0710-488a-98ea-0ff7654f1982","year":2023},"citing_paper":{"arxiv_id":"2507.00754","last_updated":"2025-07-08T19:44:08Z","snapshot_observed_at":"2026-08-07T06:17:54.949084Z","submitted_at":"2025-07-01T13:58:21Z","title":"Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:17:06.952393Z"},"links":{"cited_paper":"/paper/2310.12973","citing_paper":"/paper/2507.00754"},"observation_digest":"sha256:22b5504a0a239a5e18de6e171d7028a02307aaac20e7650aef336d326e772156","observation_id":"7df17b61-bd53-4908-bfe4-0825ff4c8666","resolution":{"observed_at":"2026-08-06T21:17:16.472633Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2310.12973/citation-record","integrity":"/paper/2310.12973/integrity","json":"/paper/2310.12973/citation-record.json","paper":"/paper/2310.12973"},"outbound":[],"paper":{"arxiv_id":"2310.12973","last_updated":"2024-05-06T15:45:30Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T22:14:28.327485Z","submitted_at":"2023-10-19T17:59:05Z","title":"Frozen Transformers in Language Models Are Effective Visual Encoder Layers"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.12973."}