{"as_of":"2026-08-09T13:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:20874f6b087acb10c1cd8cc656cc2b1292865f6ecb9c94b04d5e6a846cc2ff1e","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":"2026-08-09T06:31:02.800959+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":"2026-08-06T17:56:03.770740Z","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":"2026-05-15T05:19:45.937243Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.10666","last_updated":"2022-07-21T17:59:56Z","snapshot_observed_at":"2026-07-06T13:33:54.916595Z","submitted_at":"2022-07-21T17:59:56Z","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.10666","snapshot_observed_at":"2026-08-06T17:56:03.770740Z","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09562","last_updated":"2025-07-13T10:10:17Z","snapshot_observed_at":"2026-08-09T04:04:11.824480Z","submitted_at":"2025-07-13T10:10:17Z","title":"Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T17:56:03.770740Z"},"links":{"cited_paper":"/paper/2207.10666","citing_paper":"/paper/2507.09562"},"observation_digest":"sha256:ae86ab4f968362169e4e65691798f0e15f61b0fe556e80c8e0e0d24e6e337a54","observation_id":"e8bc9b41-a176-45d1-a94b-bf0ef87e5a4a","resolution":{"observed_at":"2026-08-06T17:56:03.770740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.10666","last_updated":"2022-07-21T17:59:56Z","snapshot_observed_at":"2026-07-06T13:33:54.916595Z","submitted_at":"2022-07-21T17:59:56Z","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","version":1},"cited_work":{"arxiv_id":"2207.10666","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.10666","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2207.10666 , year=","venue":null,"work_id":"173ba8b0-0b3d-429e-9ccf-360db5fafd17","year":null},"citing_paper":{"arxiv_id":"2605.14689","last_updated":"2026-05-14T11:03:29Z","snapshot_observed_at":"2026-07-06T23:26:04.286377Z","submitted_at":"2026-05-14T11:03:29Z","title":"Are Candidate Models Really Needed for Active Learning?","version":1},"reference_index":147,"source":"arxiv_source","source_observed_at":"2026-05-15T05:18:49.394115Z"},"links":{"cited_paper":"/paper/2207.10666","citing_paper":"/paper/2605.14689"},"observation_digest":"sha256:1b829c6fcb930798853a3f9dd4ac5668c1c91b0651e054d9f3075630509995e8","observation_id":"c2a19a2e-cb97-4cf1-a40f-bcc71991ff99","resolution":{"observed_at":"2026-05-15T05:19:45.940181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2207.10666/citation-record","integrity":"/paper/2207.10666/integrity","json":"/paper/2207.10666/citation-record.json","paper":"/paper/2207.10666"},"outbound":[],"paper":{"arxiv_id":"2207.10666","last_updated":"2022-07-21T17:59:56Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T13:33:54.916595Z","submitted_at":"2022-07-21T17:59:56Z","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2207.10666."}