{"as_of":"2026-08-17T01:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:79510cafe71c18162e63ef30737c5d466634851ccbd73a26270bf0b98b7a47f7","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T21:51:47.392219Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T21:44:57.346969Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.05199","last_updated":"2024-01-10T14:50:46Z","snapshot_observed_at":"2026-08-16T18:28:54.300886Z","submitted_at":"2024-01-10T14:50:46Z","title":"Monte Carlo Tree Search for Recipe Generation using GPT-2","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05199","snapshot_observed_at":"2026-08-15T21:51:47.392219Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08747","last_updated":"2025-05-13T17:01:21Z","snapshot_observed_at":"2026-08-15T22:25:53.692968Z","submitted_at":"2025-05-13T17:01:21Z","title":"Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:51:47.392219Z"},"links":{"cited_paper":"/paper/2401.05199","citing_paper":"/paper/2505.08747"},"observation_digest":"sha256:6a99da182a21f4588cb6859b63b6a82644cb506c8826892d48d5d72f761a1b87","observation_id":"55347b83-ca73-4f79-b543-aac66c2c847b","resolution":{"observed_at":"2026-08-15T21:51:47.392219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05199","last_updated":"2024-01-10T14:50:46Z","snapshot_observed_at":"2026-08-16T18:28:54.300886Z","submitted_at":"2024-01-10T14:50:46Z","title":"Monte Carlo Tree Search for Recipe Generation using GPT-2","version":1},"cited_work":{"arxiv_id":"2401.05199","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.05199","snapshot_observed_at":"2026-08-06T21:44:57.346969Z","title":"Monte Carlo Tree Search for Recipe Generation using GPT-2","venue":"cs.CL","work_id":"ff068e8f-b3c6-486d-a416-c168291d8d2b","year":2024},"citing_paper":{"arxiv_id":"2506.23527","last_updated":"2025-06-30T05:27:11Z","snapshot_observed_at":"2026-08-16T11:08:14.008583Z","submitted_at":"2025-06-30T05:27:11Z","title":"On Recipe Memorization and Creativity in Large Language Models: Is Your Model a Creative Cook, a Bad Cook, or Merely a Plagiator?","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T21:44:56.707221Z"},"links":{"cited_paper":"/paper/2401.05199","citing_paper":"/paper/2506.23527"},"observation_digest":"sha256:b45fdda0c955e494ae679f33e60f4932db0f0985eb31ae3e19adc38ec7affc7f","observation_id":"8136f9f7-7fc9-454d-814d-eabe25dac71a","resolution":{"observed_at":"2026-08-06T21:44:57.477600Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.05199/citation-record","integrity":"/paper/2401.05199/integrity","json":"/paper/2401.05199/citation-record.json","paper":"/paper/2401.05199"},"outbound":[],"paper":{"arxiv_id":"2401.05199","last_updated":"2024-01-10T14:50:46Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T18:28:54.300886Z","submitted_at":"2024-01-10T14:50:46Z","title":"Monte Carlo Tree Search for Recipe Generation using GPT-2"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.05199."}