{"as_of":"2026-08-06T02:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:208dac055e0d426e10f7daa988cc750598247f7a115f3fa7b229e013a88e2f33","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":"2026-08-05T06:32:48.257954+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":"2026-08-05T21:05:45.224515Z","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":"2026-05-20T11:03:13.742445Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.15720","last_updated":"2024-06-22T03:32:09Z","snapshot_observed_at":"2026-07-06T18:35:17.953523Z","submitted_at":"2024-06-22T03:32:09Z","title":"Scaling Laws for Fact Memorization of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.15720","snapshot_observed_at":"2026-08-05T21:05:45.224515Z","title":"Fingpt: Democratizing internet-scale data for financial large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.09494","last_updated":"2025-08-13T04:54:43Z","snapshot_observed_at":"2026-08-06T02:00:55.456880Z","submitted_at":"2025-08-13T04:54:43Z","title":"Learning Facts at Scale with Active Reading","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T21:05:45.224515Z"},"links":{"cited_paper":"/paper/2406.15720","citing_paper":"/paper/2508.09494"},"observation_digest":"sha256:3b5e41d1c810c8d9c74fb4056f367b69bd0b3143f199d1e27740465ca42f1da7","observation_id":"fab2e529-8271-4bb6-a0c2-cd32c520b693","resolution":{"observed_at":"2026-08-05T21:05:45.224515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.15720","last_updated":"2024-06-22T03:32:09Z","snapshot_observed_at":"2026-07-06T18:35:17.953523Z","submitted_at":"2024-06-22T03:32:09Z","title":"Scaling Laws for Fact Memorization of Large Language Models","version":1},"cited_work":{"arxiv_id":"2406.15720","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.15720","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Scaling laws for fact memorization of large language models","venue":null,"work_id":"9a7c9272-e00d-4aab-898b-d0ebe4655dad","year":2024},"citing_paper":{"arxiv_id":"2605.18732","last_updated":"2026-05-18T17:53:44Z","snapshot_observed_at":"2026-07-06T23:29:33.702647Z","submitted_at":"2026-05-18T17:53:44Z","title":"Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-20T10:59:32.371351Z"},"links":{"cited_paper":"/paper/2406.15720","citing_paper":"/paper/2605.18732"},"observation_digest":"sha256:92f8dcff81c905fd9a9d92a29a7ac182cc4b261e459c06f9ad4325765ccba5ac","observation_id":"3bd2fa5e-86b0-4234-b545-371b74f5aa04","resolution":{"observed_at":"2026-05-20T11:03:13.743817Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.15720/citation-record","integrity":"/paper/2406.15720/integrity","json":"/paper/2406.15720/citation-record.json","paper":"/paper/2406.15720"},"outbound":[],"paper":{"arxiv_id":"2406.15720","last_updated":"2024-06-22T03:32:09Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T18:35:17.953523Z","submitted_at":"2024-06-22T03:32:09Z","title":"Scaling Laws for Fact Memorization of Large 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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.15720."}