{"as_of":"2026-08-10T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1f1c6ad27b62e52dc5fc6992a61bfd6941a997730ce20fd70461c461069d3c26","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T04:52:23.186777Z","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":"2026-05-11T23:41:17.941111Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.22182","last_updated":"2024-10-29T16:19:08Z","snapshot_observed_at":"2026-08-10T06:31:22.063606Z","submitted_at":"2024-10-29T16:19:08Z","title":"Synthetic Data Generation with Large Language Models for Personalized Community Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22182","snapshot_observed_at":"2026-08-08T04:52:23.186777Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08515","last_updated":"2025-02-12T15:47:48Z","snapshot_observed_at":"2026-08-08T14:43:37.507481Z","submitted_at":"2025-02-12T15:47:48Z","title":"The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T04:52:23.186777Z"},"links":{"cited_paper":"/paper/2410.22182","citing_paper":"/paper/2502.08515"},"observation_digest":"sha256:804963c68ef46ecdaf6781b091cbeacf9f0062a912bbd601826175507482c28f","observation_id":"977ed8e4-0a1e-4c75-b0a3-7f64d4ae735e","resolution":{"observed_at":"2026-08-08T04:52:23.186777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22182","last_updated":"2024-10-29T16:19:08Z","snapshot_observed_at":"2026-08-10T06:31:22.063606Z","submitted_at":"2024-10-29T16:19:08Z","title":"Synthetic Data Generation with Large Language Models for Personalized Community Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22182","snapshot_observed_at":"2026-08-07T05:05:37.497741Z","title":"Synthetic Data Generation with Large Language Models for Personalized Community Question Answering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.08750","last_updated":"2025-06-10T12:45:12Z","snapshot_observed_at":"2026-08-10T18:45:55.715122Z","submitted_at":"2025-06-10T12:45:12Z","title":"Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.497741Z"},"links":{"cited_paper":"/paper/2410.22182","citing_paper":"/paper/2506.08750"},"observation_digest":"sha256:9c7f9b188d472a1974715bf656526fe55d43634a24a36eb4e159b643b8ec36f7","observation_id":"6a04ced7-f87a-4570-9681-90d20544b728","resolution":{"observed_at":"2026-08-07T05:05:37.497741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22182","last_updated":"2024-10-29T16:19:08Z","snapshot_observed_at":"2026-08-10T06:31:22.063606Z","submitted_at":"2024-10-29T16:19:08Z","title":"Synthetic Data Generation with Large Language Models for Personalized Community Question Answering","version":1},"cited_work":{"arxiv_id":"2410.22182","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.22182","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2410.22182 (2024)","venue":null,"work_id":"a10a7b95-3c94-4101-be39-a4f749f480c6","year":2024},"citing_paper":{"arxiv_id":"2604.26048","last_updated":"2026-04-28T18:33:21Z","snapshot_observed_at":"2026-07-06T23:11:48.524898Z","submitted_at":"2026-04-28T18:33:21Z","title":"BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T16:28:40.577563Z"},"links":{"cited_paper":"/paper/2410.22182","citing_paper":"/paper/2604.26048"},"observation_digest":"sha256:0583f6e301bd7744c75ea2825827afbf93be4668c6c5fc701c8fbea7552d933a","observation_id":"115cde84-d708-48d7-83f3-be0e0e4fbcff","resolution":{"observed_at":"2026-05-11T23:41:17.943850Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.22182/citation-record","integrity":"/paper/2410.22182/integrity","json":"/paper/2410.22182/citation-record.json","paper":"/paper/2410.22182"},"outbound":[],"paper":{"arxiv_id":"2410.22182","last_updated":"2024-10-29T16:19:08Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-10T06:31:22.063606Z","submitted_at":"2024-10-29T16:19:08Z","title":"Synthetic Data Generation with Large Language Models for Personalized Community Question Answering"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.22182."}