{"as_of":"2026-08-14T22:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:afbf9b8dc56ad001f73df0a1cfd8feb82d942951c3597496159a7037fdcf98c8","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T14:42:38.767735Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":4,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.10379","last_updated":"2024-05-27T19:54:44Z","snapshot_observed_at":"2026-08-14T11:10:53.814776Z","submitted_at":"2024-02-16T00:10:26Z","title":"DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10379","snapshot_observed_at":"2026-08-12T14:42:38.767735Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14962","last_updated":"2024-12-23T19:01:23Z","snapshot_observed_at":"2026-08-14T00:21:28.644750Z","submitted_at":"2024-11-22T14:21:18Z","title":"LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T14:42:38.767735Z"},"links":{"cited_paper":"/paper/2402.10379","citing_paper":"/paper/2411.14962"},"observation_digest":"sha256:3247a05848cf7f4d867ef8b192716103dbaaef5184ef449f312f81cb5934a8bd","observation_id":"7c07bb73-b6f6-4ad6-bce5-024e5cfd8cde","resolution":{"observed_at":"2026-08-12T14:42:38.767735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10379","last_updated":"2024-05-27T19:54:44Z","snapshot_observed_at":"2026-08-14T11:10:53.814776Z","submitted_at":"2024-02-16T00:10:26Z","title":"DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10379","snapshot_observed_at":"2026-08-09T10:50:12.456777Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04362","last_updated":"2025-02-05T04:52:57Z","snapshot_observed_at":"2026-08-13T20:13:19.109235Z","submitted_at":"2025-02-05T04:52:57Z","title":"LLMs can be easily Confused by Instructional Distractions","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-09T10:50:12.456777Z"},"links":{"cited_paper":"/paper/2402.10379","citing_paper":"/paper/2502.04362"},"observation_digest":"sha256:35f95bef541dbfa1252f754a9e9e6869a167fbebff0fb14f553838bb32eecaeb","observation_id":"1cdd079b-9d52-4a0f-8508-4a4a57a6cc9c","resolution":{"observed_at":"2026-08-09T10:50:12.456777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10379","last_updated":"2024-05-27T19:54:44Z","snapshot_observed_at":"2026-08-14T11:10:53.814776Z","submitted_at":"2024-02-16T00:10:26Z","title":"DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10379","snapshot_observed_at":"2026-08-07T05:05:37.446229Z","title":"DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows","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":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:37.446229Z"},"links":{"cited_paper":"/paper/2402.10379","citing_paper":"/paper/2506.08750"},"observation_digest":"sha256:ca68156e9c17a71be7b84e97defbe33c751ab9ea9c2f2a779a1347add1fa2c5d","observation_id":"0d177d54-1935-4f31-9909-7737b8546399","resolution":{"observed_at":"2026-08-07T05:05:37.446229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10379","last_updated":"2024-05-27T19:54:44Z","snapshot_observed_at":"2026-08-14T11:10:53.814776Z","submitted_at":"2024-02-16T00:10:26Z","title":"DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows","version":2},"cited_work":{"arxiv_id":"2402.10379","doi":"10.48550/arxiv.2402.10379","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.10379","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv:2402.10379 (2024)","venue":"arXiv (Cornell University)","work_id":"3456abe7-a0b3-4361-82c4-ff11fd1f8f9d","year":2024},"citing_paper":{"arxiv_id":"2605.25835","last_updated":"2026-05-25T13:30:38Z","snapshot_observed_at":"2026-08-14T07:44:57.190691Z","submitted_at":"2026-05-25T13:30:38Z","title":"Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T23:07:08.124993Z"},"links":{"cited_paper":"/paper/2402.10379","citing_paper":"/paper/2605.25835"},"observation_digest":"sha256:80880cf69ebb52fc8322542f0cf498f7cc290907645f1b523f12678672f92af8","observation_id":"cfdf6b95-24bb-4510-832c-a32450c7d7f0","resolution":{"observed_at":"2026-06-29T23:14:00.956922Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.10379/citation-record","integrity":"/paper/2402.10379/integrity","json":"/paper/2402.10379/citation-record.json","paper":"/paper/2402.10379"},"outbound":[],"paper":{"arxiv_id":"2402.10379","last_updated":"2024-05-27T19:54:44Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-14T11:10:53.814776Z","submitted_at":"2024-02-16T00:10:26Z","title":"DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.10379."}