{"as_of":"2026-08-20T07:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c95755f53fc16e18f73c2c4e94d346bae56d7f67630dd8c1a99d6cc777efefe2","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":"2026-08-20T06:33:59.587034+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":"2026-08-16T05:38:37.248750Z","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":"2026-07-04T00:29:16.408752Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02255","last_updated":"2024-02-03T20:22:54Z","snapshot_observed_at":"2026-08-19T12:37:33.164648Z","submitted_at":"2024-02-03T20:22:54Z","title":"Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02255","snapshot_observed_at":"2026-08-16T05:38:37.248750Z","title":"Frequency explains the inverse correlation of large language models’ size, training data amount, and surprisal’s fit to reading times.arXiv preprint arXiv:2402.02255, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.21047","last_updated":"2025-04-29T00:17:53Z","snapshot_observed_at":"2026-08-19T02:05:08.187474Z","submitted_at":"2025-04-29T00:17:53Z","title":"Model Connectomes: A Generational Approach to Data-Efficient Language Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:37.248750Z"},"links":{"cited_paper":"/paper/2402.02255","citing_paper":"/paper/2504.21047"},"observation_digest":"sha256:c08093e8c9ccd8f0f0058ca1be840ff1c0e940270cfc45ac00b9ffab23aee416","observation_id":"2387bb5e-23d0-46cc-be17-ba414cad24d3","resolution":{"observed_at":"2026-08-16T05:38:37.248750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02255","last_updated":"2024-02-03T20:22:54Z","snapshot_observed_at":"2026-08-19T12:37:33.164648Z","submitted_at":"2024-02-03T20:22:54Z","title":"Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times","version":1},"cited_work":{"arxiv_id":"2402.02255","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02255","snapshot_observed_at":"2026-07-04T00:29:16.408752Z","title":null,"venue":null,"work_id":"27beae2e-edcc-44de-afd2-2737b653b856","year":null},"citing_paper":{"arxiv_id":"2606.19236","last_updated":"2026-06-17T16:13:42Z","snapshot_observed_at":"2026-08-14T08:22:53.675389Z","submitted_at":"2026-06-17T16:13:42Z","title":"STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T21:09:30.899636Z"},"links":{"cited_paper":"/paper/2402.02255","citing_paper":"/paper/2606.19236"},"observation_digest":"sha256:de96ae2b2601966a43b82b1b2e9dc90158e3526af061e98711e72b9532289a79","observation_id":"8b9ba830-2918-4c61-a258-d0f643ce8d6f","resolution":{"observed_at":"2026-07-04T00:29:16.410919Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.02255/citation-record","integrity":"/paper/2402.02255/integrity","json":"/paper/2402.02255/citation-record.json","paper":"/paper/2402.02255"},"outbound":[],"paper":{"arxiv_id":"2402.02255","last_updated":"2024-02-03T20:22:54Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-19T12:37:33.164648Z","submitted_at":"2024-02-03T20:22:54Z","title":"Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.02255."}