{"as_of":"2026-08-18T06:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9133bffd26353f2ae84a52cd7151a2e13680782cde082335b3341d56ae60ceb","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:16:11.563307Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.05137/citation-record","integrity":"/paper/2505.05137/integrity","json":"/paper/2505.05137/citation-record.json","paper":"/paper/2505.05137"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:16:12.133756Z","title":"Anomaly detect ion: A survey","venue":null,"work_id":"d42b324f-ee1f-407f-a0ec-9d0b1f759cb7","year":2009},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.390082Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:ffd8c1f06ece5c08334f8dd8e0e11f04705ae68de86ed79b238d99050c9ea616","observation_id":"a4a599e2-96a8-4177-a154-2b0fee390ad1","resolution":{"observed_at":"2026-08-15T23:16:12.139077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.118951Z","title":"N., and Hu, J","venue":null,"work_id":"aa54cddd-02ff-449d-a168-3a0f327dab1e","year":2016},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.395287Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:9f60da3d5f6223a2c73ab7595f3483b707458cda4a2dcce4ae34c6c3e969c0ee","observation_id":"791de97e-31e7-4599-abfc-3b125bdff4b5","resolution":{"observed_at":"2026-08-15T23:16:12.123499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.103409Z","title":"Outliers in Statistical Data","venue":null,"work_id":"20b6b513-c6ed-414b-9dff-720f827e6a7b","year":1994},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.400137Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:f8a576eba726b9cf74b03e279a40baec5a359a7c589f04a36e36dbe42738a7ff","observation_id":"3ead9ed3-c8cc-4558-9f34-417de2ed2fe4","resolution":{"observed_at":"2026-08-15T23:16:12.108651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.087258Z","title":null,"venue":null,"work_id":"ebc777e6-3e72-418b-aab7-a61ae6cb62f4","year":2013},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.405620Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:5fd2b3310228f1a355cbd855bffc5aa0de9d4bf42ee9546d283c6755f3bbc4f6","observation_id":"24f74d8a-f0e5-4ade-a6be-9cca33109f55","resolution":{"observed_at":"2026-08-15T23:16:12.092017Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.072053Z","title":"C., Shawe-Taylor, J., Smola, A","venue":null,"work_id":"f40221ae-686d-4caa-b4df-aa7ca70082ea","year":2001},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.410764Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:176d2875ef15c6c1cb51409d388dc5f810f48cf815819c2b7f2cfd2a5f7ba541","observation_id":"8f264a17-a512-4ed7-9904-5722e936afca","resolution":{"observed_at":"2026-08-15T23:16:12.076872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.057152Z","title":"T., Ting, K","venue":null,"work_id":"37b122c7-4cf6-4b16-a475-a02452cc38f2","year":2008},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.415953Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:f5f7b535175fbc4eb65ef8585c28c8357e50abd3fd185a6b3efe41527f7d4eeb","observation_id":"c759cf31-18d2-45bd-ad61-3aefa85ac4d4","resolution":{"observed_at":"2026-08-15T23:16:12.061951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.041549Z","title":"Anomaly detection using auto encoders with nonlinear dimensionality reduction","venue":null,"work_id":"3f987f5c-4818-4995-8bd5-d9476f082111","year":2014},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.421737Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:8ad9038ba2b48dfd510ddbc5708149c22fbeb8482342f00b75d96977fa63a982","observation_id":"f7fcfa42-3c38-4212-b3ad-a5e8008f2be9","resolution":{"observed_at":"2026-08-15T23:16:12.046582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.025858Z","title":"P ., and Welling, M","venue":null,"work_id":"78235930-951d-4e62-b384-b0107c0175ff","year":2014},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.426928Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:590f6fb0cfc0d15239aa5f90810d53afd7cf418af8983008f4ae441908726189","observation_id":"4ca1b665-5c65-4684-8af5-ea4eed3c665c","resolution":{"observed_at":"2026-08-15T23:16:12.030741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:12.008856Z","title":"Generative Adversarial Nets","venue":null,"work_id":"5477920a-e907-49a0-8606-b3e1ad0b3041","year":2014},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.431898Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:79252c8cd5beb2f0046616b1e4b14f1fb1aecae5f854fc68bcf5dbfff57d90b0","observation_id":"f4431b08-7fd7-4763-808c-a16c970a2048","resolution":{"observed_at":"2026-08-15T23:16:12.015061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.993882Z","title":"Understanding posterior collapse in g enerative latent variable models","venue":null,"work_id":"7d3b276e-8ecd-4e0d-9fc9-d3bd45d4f5e4","year":2019},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.436484Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:1ed8f32abaeec5bf5bde8cf88aba0558f311acbee947ff641da27e187a59137e","observation_id":"e7ece331-3109-403f-ba02-c2e081b4ab77","resolution":{"observed_at":"2026-08-15T23:16:11.998809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.978124Z","title":"Towards Principled Method s for Train- ing GANs","venue":null,"work_id":"184ebd4f-d222-4641-8064-b23b13e33d2e","year":2017},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.441327Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:4e9c989d70f88ba7143eb4a32f1931cc19dcd453a9bea60d498fa139e09e6fe8","observation_id":"83ad234a-ac60-4463-b05a-db46bd0f8a0c","resolution":{"observed_at":"2026-08-15T23:16:11.983303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.962990Z","title":"Deep Unsupervised Learning using Nonequi- librium Thermodynamics","venue":null,"work_id":"f7a2bfaf-3787-485b-96c5-75eb0c90afb1","year":2015},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.446461Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:c6c7a48fb620ac4005f2a7ff597ea9a1ce5d48627593d42dc146bb65baecc91e","observation_id":"248fdc06-15b7-446c-9ae2-a5982ff28dc5","resolution":{"observed_at":"2026-08-15T23:16:11.968131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.947769Z","title":"Denoising Diffusion Pro babilistic Mod- els","venue":null,"work_id":"6d354356-be68-4b27-8bc7-abfc118ae7d0","year":2020},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.451676Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:0c10beaf466e44d98cbfc570d82d00ca98a0f14507c2a5748807bace3f4be3d0","observation_id":"61d366bb-4f6d-452f-933f-f9f11e5ef1f5","resolution":{"observed_at":"2026-08-15T23:16:11.952641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.931879Z","title":"Score-based Generative Modelin g through Stochastic Differential Equations","venue":null,"work_id":"e159e5f6-b433-40a7-b4c6-9776c942497f","year":2021},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.456738Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:fe589dc61fb42f3ea1b5fd2de72f1db151d9affafd13dc635a6986f6d290ec15","observation_id":"598ef18d-dc35-4b91-b3d7-b8fef70d3273","resolution":{"observed_at":"2026-08-15T23:16:11.936928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.917053Z","title":"Q., and Dhariwal, P","venue":null,"work_id":"e0a00e01-7f43-4ac8-87c6-91c459eee30d","year":2021},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.461825Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:016aa50b4309146c3187764ed8addf214ab695eebec7a1327230b7501a055199","observation_id":"2f00cb88-caf3-40b0-9691-cbd327022fcf","resolution":{"observed_at":"2026-08-15T23:16:11.921904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.901160Z","title":"DiffWave: A V ersatile Diffusion Model f or Audio Synthesis","venue":null,"work_id":"ae1579fd-cbbd-4c51-b0c4-d7c205e06ff5","year":2021},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.466342Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:57dc665ba8de18e3a94839cdec8a5dfd5442d1c511dc2be5337ef8576de135b8","observation_id":"16cc9bbe-2362-420d-8e7f-8e71ca00de7b","resolution":{"observed_at":"2026-08-15T23:16:11.906150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.883659Z","title":"L., et al","venue":null,"work_id":"f373d002-b914-4437-baed-2597a1e8fb40","year":2022},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.471134Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:af100b286fef825ad735b7435c5f04f1114f6794aec9080cbb5154f35d91cd53","observation_id":"b47b98e5-d943-40c1-93a1-8b29c1b77de0","resolution":{"observed_at":"2026-08-15T23:16:11.889820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04306","last_updated":"2022-10-05T13:54:33Z","snapshot_observed_at":"2026-08-16T17:16:10.422203Z","submitted_at":"2022-03-08T12:35:07Z","title":"Diffusion Models for Medical Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04306","snapshot_observed_at":"2026-08-15T23:16:11.475780Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.475780Z"},"links":{"cited_paper":"/paper/2203.04306","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:5a2f412697d9dae10f4f21d9cf52216b887c245cea47ee2c6baa10d91bd9f387","observation_id":"54ef16af-21ab-4e5d-aa6b-f874d6d64d88","resolution":{"observed_at":"2026-08-15T23:16:11.475780Z","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":"2026-08-15T23:16:11.867193Z","title":"Anomaly Detection in Networks via Score-b ased Diffusion Models","venue":null,"work_id":"19447cf2-acda-426b-84d7-47643a54afd6","year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.480544Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:10ef18ef627079c12a6e8763891e490694538c1157077f7e4f3b31acb8cc1d7a","observation_id":"d1e31faf-2189-4c19-97d8-dc7571d6de89","resolution":{"observed_at":"2026-08-15T23:16:11.872277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.851514Z","title":"High-resolution image synthesis wi th latent diffusion models","venue":null,"work_id":"5b999428-343b-4622-83ac-a1fb6b58b450","year":2022},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.485330Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:465e770e942501ae2f5f21628724445fc4b75daab75481cf1f9b330ccf024346","observation_id":"622e8eed-cf1e-4425-8947-ea051a5a4ccc","resolution":{"observed_at":"2026-08-15T23:16:11.856742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17900","last_updated":"2024-04-27T13:13:27Z","snapshot_observed_at":"2026-08-16T15:13:43.418892Z","submitted_at":"2024-04-27T13:13:27Z","title":"Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17900","snapshot_observed_at":"2026-08-15T23:16:11.490114Z","title":"Masked diffusion posterior sampling for u nsupervised anomaly detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.490114Z"},"links":{"cited_paper":"/paper/2404.17900","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:26dd767d06a699aa1c9af930796c66fad21808a5b8d6124f7741f54a4dd8cb00","observation_id":"e5a4bb46-0001-491a-bab6-95d69691989f","resolution":{"observed_at":"2026-08-15T23:16:11.490114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15956","last_updated":"2023-12-03T14:48:59Z","snapshot_observed_at":"2026-08-16T15:29:47.038946Z","submitted_at":"2023-05-25T11:54:58Z","title":"Anomaly Detection with Conditioned Denoising Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15956","snapshot_observed_at":"2026-08-15T23:16:11.495573Z","title":"Anomaly Detection with Condition ed Denoising Diffusion Models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.495573Z"},"links":{"cited_paper":"/paper/2305.15956","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:a2e49f582edd0891e2bfb93cc8bd7add250da5711970d9ba28e28567dd51ecc3","observation_id":"8ab5a58d-9ba9-4766-bc7c-413e93b0aeec","resolution":{"observed_at":"2026-08-15T23:16:11.495573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08800","last_updated":"2023-10-30T06:23:59Z","snapshot_observed_at":"2026-08-16T14:52:29.736990Z","submitted_at":"2023-10-13T01:18:41Z","title":"DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08800","snapshot_observed_at":"2026-08-15T23:16:11.501387Z","title":"DDMT: Denoising diffusion mask transfo rmer for multi- variate time series anomaly detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.501387Z"},"links":{"cited_paper":"/paper/2310.08800","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:599ada66a7fa639386addc6d1b61eff4b88c4f64f1844d6a6a096e65a5435bbb","observation_id":"0e59b495-2010-465d-87c2-e911352ca7ce","resolution":{"observed_at":"2026-08-15T23:16:11.501387Z","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":"2026-08-15T23:16:11.836549Z","title":"DiffAD: Denoising diffusion-based anoma ly detection for time series","venue":null,"work_id":"f3765fda-9099-4e2e-b55f-1de93db706fb","year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.507436Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:940380b98f07482d1ce4c69185b870892a840ea342721aac28fe87de7c8eab1d","observation_id":"2fe9b34e-372e-4887-9b51-a283487e472b","resolution":{"observed_at":"2026-08-15T23:16:11.841528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.819852Z","title":"Feature prediction diffusion model for v ideo anomaly detection","venue":null,"work_id":"826c4e50-3924-4111-9446-bf48f138e13a","year":2024},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.513095Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:bde2d0ed196df2bdb1816dc4d82c0e2209894b382f013bf9369eae62ef981b47","observation_id":"c4a4a33f-45ea-4e8e-afc9-3ceda9f61b26","resolution":{"observed_at":"2026-08-15T23:16:11.825433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18593","last_updated":"2025-03-25T03:01:44Z","snapshot_observed_at":"2026-08-16T22:02:20.302461Z","submitted_at":"2023-05-29T20:19:45Z","title":"On Diffusion Modeling for Anomaly Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18593","snapshot_observed_at":"2026-08-15T23:16:11.517840Z","title":"On Diffusion Modeling for Anomal y Detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.517840Z"},"links":{"cited_paper":"/paper/2305.18593","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:b2fc906040515b0a318ae6142ba539db54d9b01db89ab38e2bb3f6cb0e18adab","observation_id":"91c96fcf-ab4e-49a0-886b-56a89e52dd64","resolution":{"observed_at":"2026-08-15T23:16:11.517840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04382","last_updated":"2023-12-07T15:51:19Z","snapshot_observed_at":"2026-08-16T14:36:48.225878Z","submitted_at":"2023-12-07T15:51:19Z","title":"Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04382","snapshot_observed_at":"2026-08-15T23:16:11.523016Z","title":"Adversarial denoising diffusion models f or unsupervised anomaly detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.523016Z"},"links":{"cited_paper":"/paper/2312.04382","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:38e8a250dd346692b158b14460899e62d84863ed2ec688c03b3f54a30661c24a","observation_id":"52a18888-da78-41f6-9e60-6fdeebcf4764","resolution":{"observed_at":"2026-08-15T23:16:11.523016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.06959","last_updated":"2023-07-11T11:42:13Z","snapshot_observed_at":"2026-08-17T16:38:27.542869Z","submitted_at":"2022-10-13T12:37:22Z","title":"A Survey on Explainable Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.06959","snapshot_observed_at":"2026-08-15T23:16:11.528603Z","title":"A Survey on Explainable Anomaly Detection","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.528603Z"},"links":{"cited_paper":"/paper/2210.06959","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:8dfd8a8f05a3070c8f65b8e96c0ca4e739fdbddc548b9a38b726fac2803b8725","observation_id":"d374e9d8-239d-4577-8906-1ceac0cf58ce","resolution":{"observed_at":"2026-08-15T23:16:11.528603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.06670","last_updated":"2025-08-15T23:09:56Z","snapshot_observed_at":"2026-08-17T21:54:03.937205Z","submitted_at":"2023-02-13T20:17:41Z","title":"Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.06670","snapshot_observed_at":"2026-08-15T23:16:11.533538Z","title":"Explainable Anomaly Detection in Image s and Videos: A Survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.533538Z"},"links":{"cited_paper":"/paper/2302.06670","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:09c48031205d07e103b525d62c83a7a2461f8695324f2c7c3a090f57b1b457ee","observation_id":"16be4115-ad30-4a81-859b-794bfc45ba51","resolution":{"observed_at":"2026-08-15T23:16:11.533538Z","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":"2026-08-15T23:16:11.803104Z","title":"Learning important features thro ugh propagating activation differences","venue":null,"work_id":"06c6d5ec-2bc4-4fe4-8783-7951acb70fcd","year":2017},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.538534Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:57e7b1df38d9ce52dc632fd5a7f40b7e7d841240149449c3a065c1b99f8c49c3","observation_id":"a86e7fbf-9e90-4a4d-95d4-d2ec61633915","resolution":{"observed_at":"2026-08-15T23:16:11.808126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.787156Z","title":"Progressive distillation for f ast sampling of dif- fusion models","venue":null,"work_id":"c57e693b-9aef-4634-8acf-4e3cdc27364f","year":2022},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.543366Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:17db6970279c2d2dbd8eb5aa16b48e437a5bf2cb63efad8ffcad0c3d53acb917","observation_id":"70d0fae6-f14e-4f8e-a1e9-4ce86e1fcf28","resolution":{"observed_at":"2026-08-15T23:16:11.792243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.771055Z","title":"An image is worth 16x16 words: Tr ansformers for image recognition","venue":null,"work_id":"7a3c0853-5dd2-413d-bfba-4187ec5c0fca","year":2021},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.548263Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:dc7b3ab21f2993e5aad38b9822b83096b2437799e8f8513fe881cb04ef5f812d","observation_id":"3fcfbe32-3864-4739-9aea-ade19ade749c","resolution":{"observed_at":"2026-08-15T23:16:11.776451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:16:11.753692Z","title":"H., et al","venue":null,"work_id":"19e747ac-d669-4f78-bb5c-dbb53f5cb4ce","year":2022},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.553308Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:c6d0efb36c271ee6dbf401705cfa23af5043941074b44965fa8a308d0a6ef019","observation_id":"47355cd2-5a1a-44dd-8f51-54025b83d919","resolution":{"observed_at":"2026-08-15T23:16:11.759916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-15T23:16:11.558286Z","title":"GPT-4 Technical Report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.558286Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:240e63587b977a3ae90b0d4da3961b18c050713168ad42b373b64da842470de3","observation_id":"c0b2e51b-d2f0-4795-859e-767b7c799653","resolution":{"observed_at":"2026-08-15T23:16:11.558286Z","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":"2026-08-15T23:16:11.735036Z","title":"Diffusion model applied to cyber-secur ity anomaly detection","venue":null,"work_id":"5a971c97-8c23-47d4-b08a-62bc65e8ab58","year":2023},"citing_paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T23:16:11.563307Z"},"links":{"citing_paper":"/paper/2505.05137"},"observation_digest":"sha256:9a19865f90aa08f53ce1d6c3eb664e0e57d7771400bec83e358e187180eae63b","observation_id":"8a037aff-8259-46c3-a43d-d01cdb64f1a7","resolution":{"observed_at":"2026-08-15T23:16:11.742106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.05137","last_updated":"2025-05-08T11:19:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T17:42:58.498308Z","submitted_at":"2025-05-08T11:19:08Z","title":"Research on Anomaly Detection Methods Based on Diffusion Models"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":25},"total_outbound_references":35},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.05137."}