{"as_of":"2026-08-18T09:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:113f3c112d53e5a11a351ceb2edf16736a9c073e7b456e304d93eaaec63e6547","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:32:38.921363Z","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":"2026-07-02T22:57:26.356493Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-12T19:51:59.090185Z","title":"Self-improving diffusion models with synthetic data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10257","last_updated":"2025-08-29T13:10:29Z","snapshot_observed_at":"2026-08-15T06:09:40.190346Z","submitted_at":"2024-11-15T15:04:04Z","title":"Guiding a diffusion model using sliding windows","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:51:59.090185Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2411.10257"},"observation_digest":"sha256:04e6c247987ce6a1759a1a237d8c164831b426f9541b1a0a6442996ab60fd559","observation_id":"58bcf62a-993c-4620-80b1-7d113f91e3bb","resolution":{"observed_at":"2026-08-12T19:51:59.090185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-10T21:35:27.029224Z","title":"Self- improving diffusion models with synthetic data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.04666","last_updated":"2025-05-07T16:55:43Z","snapshot_observed_at":"2026-08-14T22:40:56.458130Z","submitted_at":"2025-01-08T18:25:50Z","title":"Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:35:27.029224Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2501.04666"},"observation_digest":"sha256:7adc393ce98e8459aa8f5153ee73c51668ede0959ffd10c7865c98738f1076b5","observation_id":"72824a8d-669b-4d9e-a0b7-621962ed74fc","resolution":{"observed_at":"2026-08-10T21:35:27.029224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-15T21:32:38.921363Z","title":"I., Agarwal, S., Collomosse, J., and Baraniuk, R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09768","last_updated":"2025-05-14T19:54:55Z","snapshot_observed_at":"2026-08-18T06:12:43.232799Z","submitted_at":"2025-05-14T19:54:55Z","title":"Self-Consuming Generative Models with Adversarially Curated Data","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T21:32:38.921363Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2505.09768"},"observation_digest":"sha256:f22b552ffaa48204ab86cdbb42c509870c0ea5f6e9a87394b6dea278ae86c75e","observation_id":"00e3b830-0673-46a0-a0bc-03f7a0b43cbb","resolution":{"observed_at":"2026-08-15T21:32:38.921363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-15T20:45:56.560020Z","title":"Self-improving diffusion models with synthetic data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.12108","last_updated":"2025-08-07T10:33:17Z","snapshot_observed_at":"2026-08-17T16:32:36.159196Z","submitted_at":"2025-05-17T18:27:15Z","title":"EarthSynth: Generating Informative Earth Observation with Diffusion Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:45:56.560020Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2505.12108"},"observation_digest":"sha256:66678900bed857c4db9778dfbee02850fdeb43cbac8e057ece8bb0db983491b2","observation_id":"90c89c3c-3095-46e4-9210-6648ff28b19c","resolution":{"observed_at":"2026-08-15T20:45:56.560020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-07T05:01:12.574668Z","title":"Self-improving diffusion models with synthetic data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10038","last_updated":"2025-06-10T22:37:39Z","snapshot_observed_at":"2026-08-09T04:36:51.228004Z","submitted_at":"2025-06-10T22:37:39Z","title":"Ambient Diffusion Omni: Training Good Models with Bad Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:01:12.574668Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2506.10038"},"observation_digest":"sha256:1218a4989d6ebdbc23b7566c6594c293f0184e25b7f5b9d6c472c0cc883d73a4","observation_id":"2743595f-189c-49df-bc6f-fe7b70363104","resolution":{"observed_at":"2026-08-07T05:01:12.574668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":"2408.16333","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-07-02T22:57:26.356493Z","title":"Self-improving diffusion models with synthetic data.arXiv preprint arXiv:2408.16333, 2024","venue":null,"work_id":"11c5d12c-c6ec-45e7-92ab-c61c0df286a6","year":2024},"citing_paper":{"arxiv_id":"2606.06020","last_updated":"2026-07-28T07:26:15Z","snapshot_observed_at":"2026-08-07T00:54:39.123135Z","submitted_at":"2026-06-04T11:10:55Z","title":"ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T02:25:08.038984Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2606.06020"},"observation_digest":"sha256:61c695fc1fdd6bfff9054b097f7a5ac30dd28194cf917af921df8232de367d93","observation_id":"4ee74af9-34e5-4895-8b66-29aacdeda418","resolution":{"observed_at":"2026-07-02T12:06:56.274386Z","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":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-02T12:24:28.037992Z","title":"Self-improving diffusion models with synthetic data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.06020","last_updated":"2026-07-28T07:26:15Z","snapshot_observed_at":"2026-08-07T00:54:39.123135Z","submitted_at":"2026-06-04T11:10:55Z","title":"ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-02T12:24:28.037992Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2606.06020"},"observation_digest":"sha256:b4f85b3b7c46b10eef769dbce833b074e12a7a01cc739f2e6b3f37974959315a","observation_id":"dd5470d8-4585-469e-96a4-28e85f94523b","resolution":{"observed_at":"2026-08-02T12:24:28.037992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":"2408.16333","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-07-02T22:57:26.356493Z","title":"Self-improving diffusion models with synthetic data.arXiv preprint arXiv:2408.16333, 2024","venue":null,"work_id":"11c5d12c-c6ec-45e7-92ab-c61c0df286a6","year":2024},"citing_paper":{"arxiv_id":"2606.06501","last_updated":"2026-05-17T05:14:31Z","snapshot_observed_at":"2026-08-17T11:27:40.150201Z","submitted_at":"2026-05-17T05:14:31Z","title":"Enhancing Malware Detection with Generative AI: Using Variational Autoencoders to Boost Machine Learning Classifiers' Performance","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T19:39:41.622990Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2606.06501"},"observation_digest":"sha256:fd84531571a401fbba84c0200b259c1deeed8bcd825dbe445ee7a516b60b5ab9","observation_id":"ff6ce845-502f-4f59-bf49-c77d04ed68d3","resolution":{"observed_at":"2026-06-30T19:45:00.861965Z","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":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":"2408.16333","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-07-02T22:57:26.356493Z","title":"Self-improving diffusion models with synthetic data.arXiv preprint arXiv:2408.16333, 2024","venue":null,"work_id":"11c5d12c-c6ec-45e7-92ab-c61c0df286a6","year":2024},"citing_paper":{"arxiv_id":"2606.08802","last_updated":"2026-06-07T19:43:22Z","snapshot_observed_at":"2026-08-14T10:52:42.278480Z","submitted_at":"2026-06-07T19:43:22Z","title":"Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T18:34:06.190384Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2606.08802"},"observation_digest":"sha256:f9962c1200edf76bbaf521881af5530c95812a760bf5a6afc96dd4189069b525","observation_id":"9f1dd55a-d2b3-4d1e-9524-5a3b81116528","resolution":{"observed_at":"2026-07-02T22:57:26.358422Z","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":"2408.16333","last_updated":"2024-08-29T08:12:18Z","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16333","snapshot_observed_at":"2026-08-03T13:23:49.423760Z","title":"arXiv preprint arXiv:2408.16333 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29122","last_updated":"2026-07-31T07:52:08Z","snapshot_observed_at":"2026-08-16T10:38:39.554870Z","submitted_at":"2026-07-31T07:52:08Z","title":"A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-03T13:23:49.423760Z"},"links":{"cited_paper":"/paper/2408.16333","citing_paper":"/paper/2607.29122"},"observation_digest":"sha256:331c366e355c73c44b30df2c5bb2277993becbc2e292de1f1e332ec41c64259d","observation_id":"e55328fd-12d4-445b-b5d7-3e0e842de33b","resolution":{"observed_at":"2026-08-03T13:23:49.423760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.16333/citation-record","integrity":"/paper/2408.16333/integrity","json":"/paper/2408.16333/citation-record.json","paper":"/paper/2408.16333"},"outbound":[],"paper":{"arxiv_id":"2408.16333","last_updated":"2024-08-29T08:12:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T13:23:00.297147Z","submitted_at":"2024-08-29T08:12:18Z","title":"Self-Improving Diffusion Models with Synthetic Data"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2408.16333."}