{"as_of":"2026-08-11T21:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:66865e816d6b4d5b7b308b74cf67933944d795e3315f1d9acbc9e85d80383e6e","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":"2026-08-11T06:34:44.6726+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":"2026-08-03T17:44:10.599812Z","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":"2406.01460","last_updated":"2024-06-04T07:36:57Z","snapshot_observed_at":"2026-08-05T11:21:30.034820Z","submitted_at":"2024-06-03T15:49:11Z","title":"MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01460","snapshot_observed_at":"2026-08-03T17:44:10.599812Z","title":"MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaus- tive Data Utilization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.08508","last_updated":"2026-05-25T15:55:31Z","snapshot_observed_at":"2026-08-08T06:36:20.481995Z","submitted_at":"2025-12-09T11:49:24Z","title":"Multi-Alignment Contrastive Learning for Enzyme--Reaction Retrieval","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T17:44:10.599812Z"},"links":{"cited_paper":"/paper/2406.01460","citing_paper":"/paper/2512.08508"},"observation_digest":"sha256:81e7c472948cc57506700c46ceecd0060908f0762d7c8ff37845837a8b30a532","observation_id":"5363147d-3a79-4559-a47e-8936bd0b1115","resolution":{"observed_at":"2026-08-03T17:44:10.599812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.01460/citation-record","integrity":"/paper/2406.01460/integrity","json":"/paper/2406.01460/citation-record.json","paper":"/paper/2406.01460"},"outbound":[],"paper":{"arxiv_id":"2406.01460","last_updated":"2024-06-04T07:36:57Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T11:21:30.034820Z","submitted_at":"2024-06-03T15:49:11Z","title":"MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.01460."}