{"as_of":"2026-08-19T01:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:09db75d25864a43145ba304d58c94c3fa73943d2597cb5e384caf541205a3f5b","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:57:41.175476Z","state":"measured"},{"denominator":62,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":62,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:42:01.884825Z","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":"2026-07-03T11:18:03.430540Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"cited_work":{"arxiv_id":"2412.06248","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.06248","snapshot_observed_at":"2026-07-03T11:18:03.430540Z","title":"arXiv preprint arXiv:2412.06248 , year=","venue":null,"work_id":"98720d48-45d2-4022-8e8c-6f36f79351f9","year":null},"citing_paper":{"arxiv_id":"2606.12671","last_updated":"2026-06-10T20:55:50Z","snapshot_observed_at":"2026-08-18T14:59:21.131835Z","submitted_at":"2026-06-10T20:55:50Z","title":"SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-27T09:39:11.685828Z"},"links":{"cited_paper":"/paper/2412.06248","citing_paper":"/paper/2606.12671"},"observation_digest":"sha256:5abdf19a6082c42d1a501b63cddfe450cc1f09a27fb2bf5b8f4427ec53e4619f","observation_id":"ac665552-2c17-49a4-a4a1-22c8f57d6113","resolution":{"observed_at":"2026-07-03T11:18:03.431845Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06248","snapshot_observed_at":"2026-08-11T12:42:01.884825Z","title":"arXiv preprint arXiv:2412.06248 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.09691","last_updated":"2026-08-10T14:56:47Z","snapshot_observed_at":"2026-08-15T06:29:47.965344Z","submitted_at":"2026-08-10T14:56:47Z","title":"Diffuse the object, keep its label: curating detector training data from a few unlabeled photographs via VLM-built 3D vegetation scenes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T12:42:01.884825Z"},"links":{"cited_paper":"/paper/2412.06248","citing_paper":"/paper/2608.09691"},"observation_digest":"sha256:13358f7eedd63b404689180a9201872be16dfd96775729ee2a95826cd5e4f0c9","observation_id":"f6028ccc-90cb-4a18-857b-7bbdf12c102e","resolution":{"observed_at":"2026-08-11T12:42:01.884825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.06248/citation-record","integrity":"/paper/2412.06248/integrity","json":"/paper/2412.06248/citation-record.json","paper":"/paper/2412.06248"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.02503","last_updated":"2023-05-24T01:39:41Z","snapshot_observed_at":"2026-08-16T15:57:33.881679Z","submitted_at":"2023-02-05T22:49:33Z","title":"Leaving Reality to Imagination: Robust Classification via Generated Datasets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.02503","snapshot_observed_at":"2026-08-11T19:57:41.017290Z","title":"Leaving reality to imagi- nation: Robust classification via generated datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.017290Z"},"links":{"cited_paper":"/paper/2302.02503","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:9cef7ae2f1aa70565ea0c53cf7ed7e1e10f67ea07aecb9d07da1241a47ac2e0b","observation_id":"a53eab42-1016-437e-8624-2210c5af6c14","resolution":{"observed_at":"2026-08-11T19:57:41.017290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2208.01636","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.371536Z","title":"A roadmap for greater public use of privacy-sensitive government data: Workshop report","venue":null,"work_id":"e1af27dd-572b-411d-a395-31e687e4b229","year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.020845Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:2ba7a381499ca64f9eb5c9cd7d1e8e373ac082ab7abf30a44ecae6a096dd34cf","observation_id":"1da7a129-7840-417a-8c54-21df3f93f617","resolution":{"observed_at":"2026-08-11T19:57:41.376424Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.677049Z","title":"An efficient privacy protection scheme for data secu- rity in video surveillance","venue":null,"work_id":"76416530-c6d0-4a0f-83fa-f54baac47271","year":2019},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.023572Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:2ff400581dbd75ea9dc80fdea126716b6c2f8e987802cf715518858a62b54a57","observation_id":"f40a67fe-5a30-4816-b6bf-352c91a75937","resolution":{"observed_at":"2026-08-11T19:57:41.679785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.669482Z","title":"Guidelines on anonymisation: Minsunderstandings related to anonymisation","venue":null,"work_id":"438f0984-4418-4b1f-9b3f-6e546b9afe5f","year":2021},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.026652Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:ab0d7a24a6cb8c88825c4a590a11edf4133e968255519a4fb390fe4bc1aa6d74","observation_id":"7385c428-d2ac-4933-acf1-1712a3ff4a46","resolution":{"observed_at":"2026-08-11T19:57:41.672621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.029421Z","title":"The pascal visual object classes (voc) challenge","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.029421Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:7b52cec03ef2ec070c7d08227b42dbf778504ab63d332b8c7ea1c1bb80ebcf17","observation_id":"7f3db775-db85-4383-b50b-c04799fb63ed","resolution":{"observed_at":"2026-08-11T19:57:41.029421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.032455Z","title":"Stylegan-human: A data-centric odyssey of human genera- tion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.032455Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:284b1c9e6522a98c39f944f02f0c4751283de91532bebb6c9dc8425607138409","observation_id":"be2c0752-f807-4284-bc5c-b613fe5d6fa4","resolution":{"observed_at":"2026-08-11T19:57:41.032455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.655191Z","title":"Humans in 4D: Reconstructing and tracking humans with transformers","venue":null,"work_id":"aaff27f2-18e0-4223-bc0d-e52463de7cf0","year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.035252Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:ca0e025c8c86853d7fa2aa50c73af2a67b689c4c9727fd1504827371c39ddf31","observation_id":"debb94f4-7c78-4220-84a7-bceca6880da1","resolution":{"observed_at":"2026-08-11T19:57:41.657890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07574","last_updated":"2023-02-15T06:26:38Z","snapshot_observed_at":"2026-08-18T05:41:28.555661Z","submitted_at":"2022-10-14T06:54:24Z","title":"Is synthetic data from generative models ready for image recognition?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07574","snapshot_observed_at":"2026-08-11T19:57:41.037858Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.037858Z"},"links":{"cited_paper":"/paper/2210.07574","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:0c4ec3c54ec9c2b3e569e67cb23217acdd4a299df11c21d5cc5ef8000fde8e39","observation_id":"167ca046-86cd-40d5-b63f-46896d7ddf23","resolution":{"observed_at":"2026-08-11T19:57:41.037858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.647916Z","title":"Ganonymization: A gan- based face anonymization framework for preserving emo- tional expressions","venue":null,"work_id":"7670fa97-d27a-461a-9a5d-f2e29513d26d","year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.040819Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:b41e213af44b5a94e4f81036abfd40e793643f905d435fbef397db0a39b739ba","observation_id":"dcda715e-3905-402a-8530-ac41aa12414a","resolution":{"observed_at":"2026-08-11T19:57:41.650660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03243","last_updated":"2024-05-06T07:51:13Z","snapshot_observed_at":"2026-08-16T13:55:08.948039Z","submitted_at":"2024-05-06T07:51:13Z","title":"Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data","version":1},"cited_work":{"arxiv_id":"2405.03243","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.03243","snapshot_observed_at":"2026-08-11T19:57:41.268599Z","title":"Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data","venue":"cs.CV","work_id":"f4463f74-42ad-423f-b9f4-31cb48beeb50","year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.043271Z"},"links":{"cited_paper":"/paper/2405.03243","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:642b5069a8c2366ec18f3837eb11e6c59c5d2b92ebcef6319297c94ee7e2f55f","observation_id":"67c9cf14-fd53-443d-8a50-8e1e40d34e35","resolution":{"observed_at":"2026-08-11T19:57:41.271845Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.046264Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.046264Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:49d17847924925a22c223a0b5999f2fa378bc3a5dd225656310999bb8b27e548","observation_id":"3281e5d2-fa09-495b-b1e5-273cdb7c60ea","resolution":{"observed_at":"2026-08-11T19:57:41.046264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.636986Z","title":"Humannorm: Learning normal diffusion model for high-quality and realistic 3d hu- man generation","venue":null,"work_id":"29803fb2-4160-4175-b08c-71aeee1fdb06","year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.049019Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:76af10b4a9a5c8efe868bc2978c12d7c8d2fe3cb634b95fd073ad0af63865037","observation_id":"fff86edd-01c6-4b32-9f61-67c3b517c6f9","resolution":{"observed_at":"2026-08-11T19:57:41.639800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.629469Z","title":"Deepprivacy2: To- wards realistic full-body anonymization","venue":null,"work_id":"014c803e-5fc1-4b9d-962c-0cfcdd0a2ffa","year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.051508Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:d61396eb227d297db6c1074f49c34ebd0be973d54da154748f0b83b347da1786","observation_id":"b23a753a-d96e-48a6-b040-a558b0a84f37","resolution":{"observed_at":"2026-08-11T19:57:41.632362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.053855Z","title":"Realistic full-body anonymization with surface- guided gans","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.053855Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:6f719c83aabe653e1d8cd6680fd76167200134db3ca787919d6bec3619977260","observation_id":"362ab6eb-17c5-4921-958f-5372165ca48f","resolution":{"observed_at":"2026-08-11T19:57:41.053855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.618644Z","title":"Guide to the uk general data protection regulation (uk gdpr).https://ico.org","venue":null,"work_id":"032efc8f-81f4-4133-8932-82b22110454f","year":2019},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.056509Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:ed387c2e53760325384ac8e1ceb5e1d7394e0d0778a0f8fe64c303817e52fd37","observation_id":"8722cb8a-9212-4f1b-aed5-2b4a3d2277e0","resolution":{"observed_at":"2026-08-11T19:57:41.621512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.611354Z","title":"Mimic-iii, a freely accessible critical care database","venue":null,"work_id":"9b4b4962-982f-45af-b1c4-7b35548ab816","year":2016},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.059004Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:7681ffff8d696126443a3fdc7ee691b41fa7286b558040765bdbc52e3dcba01e","observation_id":"0cc21fd4-610f-4f1a-95e3-153dfff43085","resolution":{"observed_at":"2026-08-11T19:57:41.614063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.061443Z","title":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.061443Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:ad4418433c30e08eec5cb09df86fac6df3d879f0f1406b5d73ef25fbb934fed9","observation_id":"c9e3ca55-9e5a-4751-b36a-9e021e860777","resolution":{"observed_at":"2026-08-11T19:57:41.061443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.600164Z","title":"Ldfa: Latent diffusion face anonymiza- tion for self-driving applications","venue":null,"work_id":"95b98401-ed0d-42eb-9d5c-e744e5da592b","year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.063893Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:f3d86377b56deac604cc4cde129d0117cf653516c81d25f6ce5278933a0f713e","observation_id":"35768071-951c-4e68-8aac-4013e4fc8193","resolution":{"observed_at":"2026-08-11T19:57:41.602946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.592691Z","title":"Abaw: Learning from synthetic data & 9 multi-task learning challenges","venue":null,"work_id":"72b35961-bfc9-498c-872b-36862f2c37d6","year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.066302Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:6903859f6f6447f68c69cc8715eba64ba218524c0ca70e7ec3f4b4f141f732ba","observation_id":"cb00b9a0-38a8-4203-85ce-2facea05a4b0","resolution":{"observed_at":"2026-08-11T19:57:41.595582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.585066Z","title":"The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale","venue":null,"work_id":"1931394a-234e-4a6b-b860-55918b85820a","year":1956},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.068830Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:8ce78c831b18414e4aedcc3ba7950cbb4e6d955546b9b80414ed16f90a76181a","observation_id":"0b4f725e-b1b0-40b3-8b62-2dd00f69e7ef","resolution":{"observed_at":"2026-08-11T19:57:41.588019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.577720Z","title":"California consumer privacy act (ccpa)","venue":null,"work_id":"f680db66-9f55-4f9a-91eb-bb1722224efd","year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.071377Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:4ef7af713e5a409c6878ec3086cf1949cecc6d237575695b2dad92106905c75b","observation_id":"d3bf6162-1b3a-482d-b56e-e311ef9abe41","resolution":{"observed_at":"2026-08-11T19:57:41.580449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.570409Z","title":"Fidler, and A","venue":null,"work_id":"48e8bb75-089c-41de-8e53-bcba17948af9","year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.074049Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:90d5bcea306c6c147dc23f5b8a31767e86239661cf12c51c2d363f189dc930f1","observation_id":"e179cd6a-709c-428e-a4ae-68cf63ce5cdb","resolution":{"observed_at":"2026-08-11T19:57:41.573025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.562380Z","title":"Reliable crowd- sourcing and deep locality-preserving learning for expres- sion recognition in the wild","venue":null,"work_id":"19c7b5ad-d781-4d79-a6d3-9d69d87c9aab","year":2017},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.076485Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:7bc9ed9d4dda217d55e354f7b5de897ea0ee98b7271af322ebcc9283733f8df8","observation_id":"4d5826a0-5c81-4d01-b62b-6d3861d2b111","resolution":{"observed_at":"2026-08-11T19:57:41.565858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.079004Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.079004Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:75aa05d2f18be77db904aec7186d71681c8207cdd6d7d58474a86799e81bb4b3","observation_id":"7a6b3c9a-00c3-4b07-bb12-1369f4a378ba","resolution":{"observed_at":"2026-08-11T19:57:41.079004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05499","last_updated":"2024-07-19T06:00:41Z","snapshot_observed_at":"2026-07-06T15:00:58.804337Z","submitted_at":"2023-03-09T18:52:16Z","title":"Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.05499","snapshot_observed_at":"2026-08-11T19:57:41.081371Z","title":"Grounding dino: Marrying dino with grounded pre-training for open-set object detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.081371Z"},"links":{"cited_paper":"/paper/2303.05499","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:33b02d709ba95d6f7863416c747b8849eee89efa61055735da6bea3b658e0615","observation_id":"acc65bb5-5d2b-40ea-b930-e3cc4c9b6995","resolution":{"observed_at":"2026-08-11T19:57:41.081371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.551224Z","title":"Large-scale celebfaces attributes (celeba) dataset","venue":null,"work_id":"7f459c64-4d16-452d-aecd-fc5e639d8e8b","year":2018},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.084128Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:92e1124e4004166fe83e45d155f7ab038c46ef5a405fbd3d586f4fe609cb0e97","observation_id":"57f1c437-c206-4f58-8a57-35be923ea59c","resolution":{"observed_at":"2026-08-11T19:57:41.554016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.543653Z","title":"The chicago face database: A free stimulus set of faces and norm- ing data","venue":null,"work_id":"a0841694-df7b-4202-932e-db6a7e2e2ba0","year":2015},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.086636Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:c19a5a56024071c085dfed78dd4ca4afca8dd60ae0f8578ff22f8e6eb5a5f5d9","observation_id":"5ec66419-212c-447d-8004-462b662288b6","resolution":{"observed_at":"2026-08-11T19:57:41.546506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04103","last_updated":"2025-01-10T15:37:26Z","snapshot_observed_at":"2026-08-18T17:01:05.276006Z","submitted_at":"2024-07-04T18:06:48Z","title":"Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04103","snapshot_observed_at":"2026-08-11T19:57:41.089135Z","title":"Ad- vances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future re- search directions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.089135Z"},"links":{"cited_paper":"/paper/2407.04103","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:7732fb548cc18dd28f9051de20528c7edd0f280ad8e6895ce4a1cab8fd1f726c","observation_id":"5cc2a052-890b-43a0-8902-8caf057e743e","resolution":{"observed_at":"2026-08-11T19:57:41.089135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00025","last_updated":"2025-08-22T11:37:26Z","snapshot_observed_at":"2026-08-18T23:43:13.829086Z","submitted_at":"2024-02-28T15:19:33Z","title":"On the Challenges and Opportunities in Generative AI","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.00025","snapshot_observed_at":"2026-08-11T19:57:41.092318Z","title":"On the challenges and opportunities in generative ai","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.092318Z"},"links":{"cited_paper":"/paper/2403.00025","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:02c2e2b7bbf46d9b2f3c8cbf99cc3b3b415cc482f17dbe10adc114c0bd184787","observation_id":"2e3c9c28-dbe7-4ca4-8a87-384365862a04","resolution":{"observed_at":"2026-08-11T19:57:41.092318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00056","last_updated":"2023-10-31T18:05:15Z","snapshot_observed_at":"2026-08-18T10:05:01.504591Z","submitted_at":"2023-10-31T18:05:15Z","title":"Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion","version":1},"cited_work":{"arxiv_id":"2311.00056","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.00056","snapshot_observed_at":"2026-08-11T19:57:41.240916Z","title":"Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion","venue":"cs.CV","work_id":"296cb74f-78e0-4e3f-b510-f0aa74931ce9","year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.095252Z"},"links":{"cited_paper":"/paper/2311.00056","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:bb336716efa1819834eabb15cd9ddebca65bc0c94a2940c191181430385c4987","observation_id":"b197a0e5-b85b-4749-9f62-ad4bc6aefcbf","resolution":{"observed_at":"2026-08-11T19:57:41.244226Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.536477Z","title":"Pyrender","venue":null,"work_id":"89b8ebbd-d162-4271-81f4-6b5be2e64a4c","year":2019},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.098310Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:0e91a31e8e320b3c5072b14e0faeda018d6827050a1a767ec97ef9e45aacc588","observation_id":"4fb31a97-f98d-41c7-85d2-6dbed462f52e","resolution":{"observed_at":"2026-08-11T19:57:41.539126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10741","last_updated":"2022-03-08T18:18:49Z","snapshot_observed_at":"2026-08-07T12:21:17.790675Z","submitted_at":"2021-12-20T18:42:55Z","title":"GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10741","snapshot_observed_at":"2026-08-11T19:57:41.101087Z","title":"Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.101087Z"},"links":{"cited_paper":"/paper/2112.10741","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:e9420e2abf5afdded111cf71469b12ad8dcd919ba388e22810a7d5634cc1bf52","observation_id":"70316d03-f394-41b2-9307-2903f26b5a90","resolution":{"observed_at":"2026-08-11T19:57:41.101087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.528908Z","title":null,"venue":null,"work_id":"03c63909-b30c-441b-9f4a-de74fd29d2d0","year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.103841Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:002c2b83f6a0af8c6c865b56c028f2edf465c67af6ed5ed1d7bcc7c434415df0","observation_id":"49d0c8c7-c544-4eca-98d0-e6384529836b","resolution":{"observed_at":"2026-08-11T19:57:41.531732Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.521405Z","title":"PerceptAnon: Exploring the human perception of image anonymization beyond pseudonymization for GDPR","venue":null,"work_id":"43ac3d96-4564-4f18-83ec-7373eb72e27e","year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.106316Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:3b34501b5428a1ce8f2c7922c3cdfde37fc7cb53d611febab44ebd8acf2f0812","observation_id":"09fab9f0-0dfa-469e-81ad-5104f63514e5","resolution":{"observed_at":"2026-08-11T19:57:41.524155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.513859Z","title":"Data and its (dis) contents: A survey of dataset development and use in ma- chine learning research","venue":null,"work_id":"a31f99fc-7da8-49f8-a22c-b08511048684","year":2021},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.108776Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:1e4182ddefb3b2610fd9f22bbc63fcc0586a5dca9dd20f53f83188c793e04ec7","observation_id":"3778efb2-ee3f-46fc-8697-538a4cca6172","resolution":{"observed_at":"2026-08-11T19:57:41.516889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.111422Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.111422Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:192720c39c566481e949ff33e837b2d425f01942b8c4bfb4c448f193ddbe50e8","observation_id":"8efd3cad-959e-4b82-bfc7-86bbc622d97d","resolution":{"observed_at":"2026-08-11T19:57:41.111422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14790","last_updated":"2024-03-21T19:09:21Z","snapshot_observed_at":"2026-08-16T14:07:33.423425Z","submitted_at":"2024-03-21T19:09:21Z","title":"Latent Diffusion Models for Attribute-Preserving Image Anonymization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14790","snapshot_observed_at":"2026-08-11T19:57:41.114055Z","title":"Latent diffusion models for attribute-preserving im- age anonymization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.114055Z"},"links":{"cited_paper":"/paper/2403.14790","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:bd00a60e90519577cd7617a965c3f66a525a9944b1bb4ac1487fab02b4159ef9","observation_id":"da6168c9-04c9-4cd5-bfaf-6ac3256cf213","resolution":{"observed_at":"2026-08-11T19:57:41.114055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.502147Z","title":"State of the art on diffusion models for visual computing","venue":null,"work_id":"eea2e536-d9c3-4cc5-8371-4612bfc35f07","year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.116700Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:e6b2d8425d468fa893677c45c9fc74ba4830e6c546fc06576e60230363c7690b","observation_id":"f12409ed-0ac8-42a4-ad2e-01a415511cce","resolution":{"observed_at":"2026-08-11T19:57:41.505416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-08-14T22:54:08.184266Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-11T19:57:41.119157Z","title":"Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.119157Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:a0f73b612b31dab9b200758266463eb0203472b84d38c82739bac3993f50c8e3","observation_id":"58c76d68-9778-437d-b69f-8a32bf0ace4e","resolution":{"observed_at":"2026-08-11T19:57:41.119157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.121830Z","title":"Zero-shot text-to-image generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.121830Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:03fa2b6de34a39e1934b4db9b7e11f1764d361bcc081dd3cdefff30a0a3d135b","observation_id":"36f52412-35ca-44b4-bd1b-de7c84afb3ab","resolution":{"observed_at":"2026-08-11T19:57:41.121830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-08-15T12:50:58.405488Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-11T19:57:41.124428Z","title":"Hierarchical text-conditional image gener- ation with clip latents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.124428Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:0460db74bb2d2bf5d94e7a65503ee0a6936a10a0f6df2d98c6ec61d3ee2c5c34","observation_id":"af7f5d13-ac21-491b-b7d8-9717b5bb22b8","resolution":{"observed_at":"2026-08-11T19:57:41.124428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.490858Z","title":"Regulation (eu) 2016/679 of the eu- ropean parliament and of the council","venue":null,"work_id":"c676df6f-4518-4feb-95cb-03dc6bc66b91","year":2016},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.127158Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:8ddf27dc59456bd6499e196bdec597ef23457a4b382aeafa8d95eeae903e4747","observation_id":"b7136347-9ba7-49c3-826d-bbb283b2de5e","resolution":{"observed_at":"2026-08-11T19:57:41.493720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.483361Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","venue":null,"work_id":"24efb4e8-85fa-45b7-9687-cb89e2f10835","year":2016},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.129582Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:bf78327313f7b96112dccaeb5e88685156470c29f0b6644add8afe2525562c8e","observation_id":"15940ece-d3b2-4b37-9513-fe7042d902ad","resolution":{"observed_at":"2026-08-11T19:57:41.486196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.6028/nist.tn.2151","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.193916Z","title":"Challenge design and lessons learned from the 2018 differential privacy challenges","venue":null,"work_id":"00301594-3819-4dc4-ba39-fe6f43125b8d","year":2018},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.132165Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:764fab02b8403e25b9d05912c1b29eccfffa4cfcdcdf97e2f8416fe5f021fa58","observation_id":"5b70e3fd-8f55-4eed-b7c5-4277d85d1cea","resolution":{"observed_at":"2026-08-11T19:57:41.198305Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.475980Z","title":"Blattmann, Dominik Lorenz, Patrick Esser, and B","venue":null,"work_id":"246e879f-e0e3-41df-a420-dac4a2957c78","year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.134751Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:7a3481b2ab07e3be8e8ff443e069255b142e0252f61b874d26a13c01af38c392","observation_id":"0e4c9942-479d-40b6-a795-0dfefe3cab4f","resolution":{"observed_at":"2026-08-11T19:57:41.478785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.137374Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.137374Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:6599bfc2bd1fecf51b0c6749466762ea40491532cfd328d0c061d43f8cbb3440","observation_id":"882e4dfa-979d-42ab-8e9d-2c6ef5f19c7c","resolution":{"observed_at":"2026-08-11T19:57:41.137374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11487","last_updated":"2022-05-23T17:42:53Z","snapshot_observed_at":"2026-08-17T05:51:02.087480Z","submitted_at":"2022-05-23T17:42:53Z","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11487","snapshot_observed_at":"2026-08-11T19:57:41.139813Z","title":"Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.139813Z"},"links":{"cited_paper":"/paper/2205.11487","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:44f7b22777019bd059d4a175818de7a56efd52b8703ab7d3084533d03b05d104","observation_id":"c8ae7653-2994-4fa6-a002-5c2fbd99413a","resolution":{"observed_at":"2026-08-11T19:57:41.139813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.142540Z","title":"Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.142540Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:64f7156ac21bbbace3da6ddb5b85985b4a1ea62a18a621249c7f38f3cd6a98ec","observation_id":"4c3744e1-13b5-490f-92a8-7310a4179367","resolution":{"observed_at":"2026-08-11T19:57:41.142540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15811","last_updated":"2024-07-22T17:23:28Z","snapshot_observed_at":"2026-08-18T13:06:14.495335Z","submitted_at":"2024-07-22T17:23:28Z","title":"Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15811","snapshot_observed_at":"2026-08-11T19:57:41.145038Z","title":"Stretching each dollar: Diffu- sion training from scratch on a micro-budget","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.145038Z"},"links":{"cited_paper":"/paper/2407.15811","citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:643a6ec236a474f4f75a0d932becb5d63b999a0328bf4414d7fca466a509eb1f","observation_id":"b6d763c1-2426-4e01-8c40-caefe13079fc","resolution":{"observed_at":"2026-08-11T19:57:41.145038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.147716Z","title":"Objects365: A large-scale, high-quality dataset for object detection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.147716Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:f0c87f7d97154df441773c647590d1ae0863e124d71c11decbd77c3cbfd8c1c7","observation_id":"77bc2489-a131-4b32-a02b-f86c030715df","resolution":{"observed_at":"2026-08-11T19:57:41.147716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.456855Z","title":"Decouple- and-sample: Protecting sensitive information in task agnos- tic data release","venue":null,"work_id":"6cc172b8-bacd-4704-918f-6157afbab691","year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.150115Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:47898605ea9000a98695c4d1a2bb82d9c0d7dd0fce90da5175a64f44362af9b4","observation_id":"4fcaf90c-917a-4fab-8a0b-d8fcb08cf304","resolution":{"observed_at":"2026-08-11T19:57:41.459816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.449117Z","title":"Privacy as- sessment on reconstructed images: Are existing evaluation metrics faithful to human perception? Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":"ad3b666d-94ae-47ed-8b72-9f64bcc55916","year":2024},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.153447Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:6193d22decfebbfa45c5eef1aa8aa2776541d9c39cbe1b4ade089f9f406d3037","observation_id":"fda4eb7f-81a6-4463-b1c3-6480b679b8a6","resolution":{"observed_at":"2026-08-11T19:57:41.452019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.441538Z","title":"Making sense of cron- bach’s alpha","venue":null,"work_id":"b19302b4-f747-403d-9a48-da40dd1fc86d","year":2011},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.156663Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:5a56c20d0c85e04620cc4c19baa6741e7c2f1bc53607d9fecf1a64036adcce05","observation_id":"bae64927-19ee-42b1-8b01-42ee23550488","resolution":{"observed_at":"2026-08-11T19:57:41.444344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.159038Z","title":"Fake it till you make it: face analysis in the wild using synthetic data alone","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.159038Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:d23fcd9b59c19b67910e3af7641e83a8f5efa175068a7a4f290d10880ba2c96f","observation_id":"351d5c1c-3e8e-44d4-a2d5-ee7e6d3ad815","resolution":{"observed_at":"2026-08-11T19:57:41.159038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.429010Z","title":"A study of face obfuscation in ima- genet","venue":null,"work_id":"0aba9885-5aed-4bf5-bdd5-f0c9d6817244","year":2022},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.161435Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:f6e5a4fd1fbbf4876c7adae0e53d2d690b1f90d9e3ec92f0a414b7256ec03539","observation_id":"f7b5d72a-22c5-4440-abbe-d6438772fb9c","resolution":{"observed_at":"2026-08-11T19:57:41.431956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.420205Z","title":"Adding conditional control to text-to-image diffusion models, 2023","venue":null,"work_id":"09aa5061-fb72-483f-8557-96776f770b46","year":2023},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.163888Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:bb00c1d7ffa24628070f9f1b58b4a6788e4aa4d6bc559f95f4e455bf6a4be72d","observation_id":"cb1336f6-3bb7-4d36-a9e2-06a82d70c1ab","resolution":{"observed_at":"2026-08-11T19:57:41.423121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.412469Z","title":"A person with no beard","venue":null,"work_id":"e3c68d75-d1f6-4765-ad3b-7ba543391083","year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.166297Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:a4417f1df78ca2b023b3bca3bc7252f53545a8f1fcc56551da5681ab81cb5227","observation_id":"cbc56aba-5149-4c87-9c5f-01312058a8f7","resolution":{"observed_at":"2026-08-11T19:57:41.415219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.404078Z","title":"9, 12 original SD RefSDDP2 Figure 9","venue":null,"work_id":"5dfff5cc-c53a-467a-9528-f72eba212701","year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.168898Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:04c70d1e9b112ca7c676473a5e4fde63d4222571d19288b54696a56b26a33867","observation_id":"43106c4d-c2b2-4510-b022-61f80d0d357a","resolution":{"observed_at":"2026-08-11T19:57:41.406852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.396050Z","title":"For each evaluation, we provide a detailed breakdown of the categories and at- tributes, present all results for each category, and include sample RefSD-generated images","venue":null,"work_id":"d4189471-d3b2-4ba8-a219-effdd6cd2413","year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.171617Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:fc2e8fcd2e09102e31e38e0d6c1c89c493535a12e170c7019c49e97c9e6a27e1","observation_id":"fcedab70-9cfb-4611-a135-ed5c4fc9a69b","resolution":{"observed_at":"2026-08-11T19:57:41.399198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:57:41.388577Z","title":null,"venue":null,"work_id":"d39a809c-cad2-4a6c-b2ad-8dfc4c9a2c94","year":null},"citing_paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:41.175476Z"},"links":{"citing_paper":"/paper/2412.06248"},"observation_digest":"sha256:33e115b2b9df2c464e0f04e3c6684f430181941e51b5ac9e69bac4be000faa9d","observation_id":"4bf7bac5-2620-444b-bf6f-d630d9124bfa","resolution":{"observed_at":"2026-08-11T19:57:41.391253Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.06248","last_updated":"2024-12-09T06:47:29Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T11:07:15.346366Z","submitted_at":"2024-12-09T06:47:29Z","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":4,"verified_fuzzy":31},"total_outbound_references":60},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2412.06248."}