{"as_of":"2026-08-09T21:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9811b5814a8f4f254bd6d88ebdfe5ab66140dc021d244e9eba80331829d63386","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T20:06:55.102341Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.18675","last_updated":"2025-07-14T21:28:12Z","snapshot_observed_at":"2026-08-09T17:02:29.829034Z","submitted_at":"2024-12-24T20:28:07Z","title":"TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.18675","snapshot_observed_at":"2026-07-11T20:06:55.102341Z","title":"TAB: Transformer attention bottlenecks enable user intervention and debugging in vision-language models.arXiv preprint arXiv:2412.18675,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04319","last_updated":"2026-07-05T14:08:59Z","snapshot_observed_at":"2026-08-09T19:18:42.431865Z","submitted_at":"2026-07-05T14:08:59Z","title":"Legible-by-Construction: Attention and End-to-End Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-11T20:06:55.102341Z"},"links":{"cited_paper":"/paper/2412.18675","citing_paper":"/paper/2607.04319"},"observation_digest":"sha256:d4880428cc00ad3d6e2b39039b0abbe80ed39dcdbb4c4ecc25ea22add4093dde","observation_id":"f5079130-d1f2-4118-a6e1-627332b5c157","resolution":{"observed_at":"2026-07-11T20:06:55.102341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.18675/citation-record","integrity":"/paper/2412.18675/integrity","json":"/paper/2412.18675/citation-record.json","paper":"/paper/2412.18675"},"outbound":[],"paper":{"arxiv_id":"2412.18675","last_updated":"2025-07-14T21:28:12Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T17:02:29.829034Z","submitted_at":"2024-12-24T20:28:07Z","title":"TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models"},"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 1 inbound Pith citation observation for arXiv:2412.18675."}