{"as_of":"2026-08-13T08:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:538f7367f84ceab79bcf9b1a0ffc15df549a9d3198676129026083eff7c57a2d","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":"2026-08-13T06:32:02.005865+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":"2026-08-12T16:30:25.730193Z","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":"2026-07-04T03:39:30.137146Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.05656","last_updated":"2024-03-24T02:43:55Z","snapshot_observed_at":"2026-08-13T06:18:55.094825Z","submitted_at":"2023-03-10T02:15:58Z","title":"EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.05656","snapshot_observed_at":"2026-08-12T16:30:25.730193Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.13428","last_updated":"2024-11-20T16:11:20Z","snapshot_observed_at":"2026-08-12T22:35:00.980822Z","submitted_at":"2024-11-20T16:11:20Z","title":"SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T16:30:25.730193Z"},"links":{"cited_paper":"/paper/2303.05656","citing_paper":"/paper/2411.13428"},"observation_digest":"sha256:9e5c9f0ba1cceae479fe58dabe15385dddd67451a0ca58e243ab9a3cdd410f0e","observation_id":"753fa515-6068-4eb8-803b-4447adcd00ef","resolution":{"observed_at":"2026-08-12T16:30:25.730193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05656","last_updated":"2024-03-24T02:43:55Z","snapshot_observed_at":"2026-08-13T06:18:55.094825Z","submitted_at":"2023-03-10T02:15:58Z","title":"EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models","version":3},"cited_work":{"arxiv_id":"2303.05656","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.05656","snapshot_observed_at":"2026-07-04T03:39:30.137146Z","title":"2303.05656 , archivePrefix=","venue":null,"work_id":"81cbd793-d142-4669-9815-c1f6fc64825c","year":null},"citing_paper":{"arxiv_id":"2606.06990","last_updated":"2026-06-05T07:28:26Z","snapshot_observed_at":"2026-08-11T16:19:45.486688Z","submitted_at":"2026-06-05T07:28:26Z","title":"Accelerating Reproducible Research in Synthetic EHR Generation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-06-27T22:38:13.324847Z"},"links":{"cited_paper":"/paper/2303.05656","citing_paper":"/paper/2606.06990"},"observation_digest":"sha256:e80b350000ce9bb37d1259c96b0d25f77dd3042c443acbc19c3756f675e7022f","observation_id":"b8dac21d-c6fb-45ce-98ef-81e29fe141a3","resolution":{"observed_at":"2026-07-02T16:27:09.603189Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05656","last_updated":"2024-03-24T02:43:55Z","snapshot_observed_at":"2026-08-13T06:18:55.094825Z","submitted_at":"2023-03-10T02:15:58Z","title":"EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models","version":3},"cited_work":{"arxiv_id":"2303.05656","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.05656","snapshot_observed_at":"2026-07-04T03:39:30.137146Z","title":"2303.05656 , archivePrefix=","venue":null,"work_id":"81cbd793-d142-4669-9815-c1f6fc64825c","year":null},"citing_paper":{"arxiv_id":"2606.20122","last_updated":"2026-06-18T11:47:13Z","snapshot_observed_at":"2026-08-13T00:55:45.195552Z","submitted_at":"2026-06-18T11:47:13Z","title":"ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research","version":1},"reference_index":182,"source":"arxiv_source","source_observed_at":"2026-06-26T17:50:16.183571Z"},"links":{"cited_paper":"/paper/2303.05656","citing_paper":"/paper/2606.20122"},"observation_digest":"sha256:23149818cfbbf43d1c95d2a554da87dd68f3fa64301c04dc67e5c233c7282424","observation_id":"db220072-54b3-4df1-8196-cf430e56d091","resolution":{"observed_at":"2026-07-04T03:39:30.138946Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05656","last_updated":"2024-03-24T02:43:55Z","snapshot_observed_at":"2026-08-13T06:18:55.094825Z","submitted_at":"2023-03-10T02:15:58Z","title":"EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.05656","snapshot_observed_at":"2026-08-01T13:54:37.024593Z","title":"2303.05656 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18979","last_updated":"2026-07-21T11:14:33Z","snapshot_observed_at":"2026-08-06T13:13:54.204332Z","submitted_at":"2026-07-21T11:14:33Z","title":"Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning","version":1},"reference_index":236,"source":"arxiv_source","source_observed_at":"2026-08-01T13:54:37.024593Z"},"links":{"cited_paper":"/paper/2303.05656","citing_paper":"/paper/2607.18979"},"observation_digest":"sha256:7b14a74fabc3d18e95a4a0faa535b198948ca2cd0b5d0c8993f319eb5ebbccd3","observation_id":"52b4adc1-c3d3-439e-b5fe-46ce03d3b1ce","resolution":{"observed_at":"2026-08-01T13:54:37.024593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2303.05656/citation-record","integrity":"/paper/2303.05656/integrity","json":"/paper/2303.05656/citation-record.json","paper":"/paper/2303.05656"},"outbound":[],"paper":{"arxiv_id":"2303.05656","last_updated":"2024-03-24T02:43:55Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T06:18:55.094825Z","submitted_at":"2023-03-10T02:15:58Z","title":"EHRDiff: Exploring Realistic EHR Synthesis with Diffusion 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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2303.05656."}