{"as_of":"2026-08-10T23:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:06ac3dbb106704680af9b6bb28c57b4bc9bc78dafdd6e5afc18c5ca8c9d0d958","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T15:07:10.068657Z","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":"2026-06-29T17:33:45.370024Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":"2305.15560","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-06-29T17:33:45.370024Z","title":"Dif- ferentially private synthetic data via foundation model apis 1: Images","venue":null,"work_id":"994650b3-079f-440c-95bf-23eb5afab515","year":2023},"citing_paper":{"arxiv_id":"2306.11644","last_updated":"2023-10-02T06:12:30Z","snapshot_observed_at":"2026-08-10T21:55:43.802039Z","submitted_at":"2023-06-20T16:14:25Z","title":"Textbooks Are All You Need","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-13T04:44:03.148223Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2306.11644"},"observation_digest":"sha256:c8a2e698d2a344211f06bc443b49a9f4d8e70f33f97f73eb61e37226535e40cb","observation_id":"2fb47446-9c3d-4c72-ab0e-3bca22d3cbf7","resolution":{"observed_at":"2026-05-13T04:44:03.233840Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-08-08T15:07:10.068657Z","title":"Differentially private synthetic data via foundation model apis 1: Images","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06555","last_updated":"2025-02-10T15:23:52Z","snapshot_observed_at":"2026-08-08T15:02:07.200879Z","submitted_at":"2025-02-10T15:23:52Z","title":"Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T15:07:10.068657Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2502.06555"},"observation_digest":"sha256:5f4682461253ede374d2cd926bac244a47c0aede38461a3721e38682d7795530","observation_id":"becaf1e2-f865-4600-9f7b-e76ae8b70f2c","resolution":{"observed_at":"2026-08-08T15:07:10.068657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-08-06T21:38:47.767141Z","title":"Differentially private synthetic data via foundation model apis 1: Images","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23855","last_updated":"2025-06-30T13:46:57Z","snapshot_observed_at":"2026-08-09T12:47:47.850210Z","submitted_at":"2025-06-30T13:46:57Z","title":"Differentially Private Synthetic Data Release for Topics API Outputs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:47.767141Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2506.23855"},"observation_digest":"sha256:f722e27e16bee37671399b17925f8f0c2f9a13ced4267cf497452e8217105c76","observation_id":"b14b69bc-eb2d-4e26-9d9b-e3513774e6d1","resolution":{"observed_at":"2026-08-06T21:38:47.767141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-08-03T18:52:59.120575Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.03238","last_updated":"2026-07-09T21:40:53Z","snapshot_observed_at":"2026-08-04T23:00:48.940171Z","submitted_at":"2025-12-02T21:14:39Z","title":"How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy","version":2},"reference_index":145,"source":"arxiv_source","source_observed_at":"2026-08-03T18:52:59.120575Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2512.03238"},"observation_digest":"sha256:66ada26eb57c1618bcf95c0a0db08476a6c1b1b1be132ca29ec60b278957774a","observation_id":"64ecbd54-3185-484b-baf6-814669e8733a","resolution":{"observed_at":"2026-08-03T18:52:59.120575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-08-03T05:48:10.499227Z","title":"Dif- ferentially private synthetic data via foundation model apis 1: Images.arXiv preprint arXiv:2305.15560, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.01607","last_updated":"2026-06-04T00:07:53Z","snapshot_observed_at":"2026-08-07T21:22:55.039761Z","submitted_at":"2026-02-02T03:54:11Z","title":"Minimax optimal differentially private synthetic data for smooth queries","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T05:48:10.499227Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2602.01607"},"observation_digest":"sha256:e60ae1fc73f08b50efb73de07d67ca4a829160eb3d4a1f7b4b0d62195aab39f7","observation_id":"b1699997-69e6-4d71-91e6-c5b9ad987603","resolution":{"observed_at":"2026-08-03T05:48:10.499227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":"2305.15560","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-06-29T17:33:45.370024Z","title":"Dif- ferentially private synthetic data via foundation model apis 1: Images","venue":null,"work_id":"994650b3-079f-440c-95bf-23eb5afab515","year":2023},"citing_paper":{"arxiv_id":"2605.17432","last_updated":"2026-05-17T12:55:11Z","snapshot_observed_at":"2026-07-06T23:28:26.397778Z","submitted_at":"2026-05-17T12:55:11Z","title":"DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-20T13:35:02.869657Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2605.17432"},"observation_digest":"sha256:4a9404a9a27e61f59312317850157299a0d20bd0c01463d35a04ed0e645728cd","observation_id":"3bacacfe-7643-4617-80c1-96c59b125717","resolution":{"observed_at":"2026-05-20T13:38:19.400206Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":"2305.15560","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-06-29T17:33:45.370024Z","title":"Dif- ferentially private synthetic data via foundation model apis 1: Images","venue":null,"work_id":"994650b3-079f-440c-95bf-23eb5afab515","year":2023},"citing_paper":{"arxiv_id":"2605.27148","last_updated":"2026-06-01T21:00:09Z","snapshot_observed_at":"2026-07-06T23:36:53.330986Z","submitted_at":"2026-05-26T15:10:35Z","title":"Landseer: Exploring the Machine Learning Defense Landscape","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-29T17:27:29.241219Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2605.27148"},"observation_digest":"sha256:70b56f01957362b61512c8f511ce710cc793781224069f7d82525f41eadc7b15","observation_id":"36164c9f-2d17-4df9-a05e-94a0f90fd65f","resolution":{"observed_at":"2026-06-29T17:33:45.371943Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15560","snapshot_observed_at":"2026-08-02T11:15:50.220869Z","title":"Dif- ferentially private synthetic data via foundation model apis 1: Images.arXiv preprint arXiv:2305.15560, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.16952","last_updated":"2026-07-20T23:44:56Z","snapshot_observed_at":"2026-08-08T15:50:07.505351Z","submitted_at":"2026-06-15T16:54:02Z","title":"Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T11:15:50.220869Z"},"links":{"cited_paper":"/paper/2305.15560","citing_paper":"/paper/2606.16952"},"observation_digest":"sha256:add9bad513c7892b27f57d4ce39ea6cdba820a93e452f8b23a09a016fab81fa7","observation_id":"340961fc-3fd2-4e5d-b866-3feef06a813f","resolution":{"observed_at":"2026-08-02T11:15:50.220869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2305.15560/citation-record","integrity":"/paper/2305.15560/integrity","json":"/paper/2305.15560/citation-record.json","paper":"/paper/2305.15560"},"outbound":[],"paper":{"arxiv_id":"2305.15560","last_updated":"2025-05-17T15:12:18Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T15:32:58.159006Z","submitted_at":"2023-05-24T23:47:26Z","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images"},"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 8 inbound Pith citation observations for arXiv:2305.15560."}