{"as_of":"2026-08-13T05:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b8a95296dd66824dfcfbd340d7eb53c6c332744c80a7725c9498761a99b44f01","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:41:15.408779Z","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":"2026-05-18T17:16:39.580526Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.02398","snapshot_observed_at":"2026-08-11T23:41:15.408779Z","title":"Diffusion model for generative image denoising,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02322","last_updated":"2024-12-03T09:38:14Z","snapshot_observed_at":"2026-08-12T15:49:22.462181Z","submitted_at":"2024-12-03T09:38:14Z","title":"Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:15.408779Z"},"links":{"cited_paper":"/paper/2302.02398","citing_paper":"/paper/2412.02322"},"observation_digest":"sha256:2a147283902675a3f14d7e1b95edfd79ae622283460a920bc9dc12e871bfdfad","observation_id":"af14117f-dfd4-4ec2-a183-23e0181888b8","resolution":{"observed_at":"2026-08-11T23:41:15.408779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.02398","snapshot_observed_at":"2026-08-10T22:59:48.962165Z","title":"Diffusion model for gen- erative image denoising,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01456","last_updated":"2024-12-31T04:32:46Z","snapshot_observed_at":"2026-08-12T07:45:37.359889Z","submitted_at":"2024-12-31T04:32:46Z","title":"SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:59:48.962165Z"},"links":{"cited_paper":"/paper/2302.02398","citing_paper":"/paper/2501.01456"},"observation_digest":"sha256:6269cb4099088508f47cf1a08c1b900b32e223a38aae42c5ec779f78473e1127","observation_id":"c3de83ca-d27f-4278-b7e3-8fb7138e88a3","resolution":{"observed_at":"2026-08-10T22:59:48.962165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.02398","snapshot_observed_at":"2026-08-06T22:18:53.295007Z","title":", author Yuan, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.22012","last_updated":"2025-06-27T08:24:55Z","snapshot_observed_at":"2026-08-12T09:59:04.395407Z","submitted_at":"2025-06-27T08:24:55Z","title":"Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-06T22:18:53.295007Z"},"links":{"cited_paper":"/paper/2302.02398","citing_paper":"/paper/2506.22012"},"observation_digest":"sha256:580b21eb57de5ffb83c13c29b0942212b453b39b326860e1ebd7cf3d939e76cb","observation_id":"9ba7fd90-ebfc-49c4-bc45-b10702eef98a","resolution":{"observed_at":"2026-08-06T22:18:53.295007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising","version":1},"cited_work":{"arxiv_id":"2302.02398","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.02398","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"72d8ad3b-289a-4759-b929-419c87f746e6","year":2023},"citing_paper":{"arxiv_id":"2509.11485","last_updated":"2026-06-08T02:11:16Z","snapshot_observed_at":"2026-08-13T01:29:16.958516Z","submitted_at":"2025-09-15T00:23:23Z","title":"Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-18T17:15:08.543454Z"},"links":{"cited_paper":"/paper/2302.02398","citing_paper":"/paper/2509.11485"},"observation_digest":"sha256:3c6a45f9cc339a5402db868bf5aa584bbe1540f29bffd96d3a2ca39c9165dd76","observation_id":"a0b0dbe0-c244-40f2-8527-749a89bcd579","resolution":{"observed_at":"2026-05-18T17:16:39.583805Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.02398","snapshot_observed_at":"2026-08-04T16:51:40.836392Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11485","last_updated":"2026-06-08T02:11:16Z","snapshot_observed_at":"2026-08-13T01:29:16.958516Z","submitted_at":"2025-09-15T00:23:23Z","title":"Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T16:51:40.836392Z"},"links":{"cited_paper":"/paper/2302.02398","citing_paper":"/paper/2509.11485"},"observation_digest":"sha256:7b77ff2e545287a2e48c2dbcfe83d8c32a63185ce83dc9c05e8e5a4c1576709d","observation_id":"563f06df-79bf-4424-be2e-392101dc6937","resolution":{"observed_at":"2026-08-04T16:51:40.836392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.02398","snapshot_observed_at":"2026-08-01T12:40:13.832758Z","title":"Diffusion model for generative image denoising,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22719","last_updated":"2026-07-21T18:45:58Z","snapshot_observed_at":"2026-08-08T04:31:13.552348Z","submitted_at":"2026-07-21T18:45:58Z","title":"$\\gamma$-Bridge: A Look-Parametric Diffusion Bridge","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T12:40:13.832758Z"},"links":{"cited_paper":"/paper/2302.02398","citing_paper":"/paper/2607.22719"},"observation_digest":"sha256:a107af43f0a740a5fcae30d24caf7f9961900a7c3ea86bf5adfef955fbf2e465","observation_id":"2731f839-e06e-4ff1-acfd-f1864e7b3990","resolution":{"observed_at":"2026-08-01T12:40:13.832758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2302.02398/citation-record","integrity":"/paper/2302.02398/integrity","json":"/paper/2302.02398/citation-record.json","paper":"/paper/2302.02398"},"outbound":[],"paper":{"arxiv_id":"2302.02398","last_updated":"2023-02-05T14:53:07Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T03:46:47.847975Z","submitted_at":"2023-02-05T14:53:07Z","title":"Diffusion Model for Generative Image Denoising"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2302.02398."}