{"as_of":"2026-08-17T14:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:475d7537732fae8ab48ee293fa6da9b59664dd91a79a38aa2bb3de4748e3c053","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T18:47:46.565365Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"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/2412.07539/citation-record","integrity":"/paper/2412.07539/integrity","json":"/paper/2412.07539/citation-record.json","paper":"/paper/2412.07539"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:47:46.960968Z","title":"Isolation f orest","venue":null,"work_id":"438190b5-9577-4cd4-bcf8-94ff314b9f92","year":2008},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.463070Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:347d3e479e4d5e7dc0c42b95c53a3b716f562fff3d3df859e442dc1438325e25","observation_id":"4395a9f1-7c6a-4d4e-9010-c4b626c3db9d","resolution":{"observed_at":"2026-08-11T18:47:46.963900Z","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":"2026-08-11T18:47:46.952219Z","title":"Estimating the support of a high-dimensional d istribution","venue":null,"work_id":"98854dd0-bce6-44d2-8ac5-691af3fab532","year":2001},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.466876Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:ff322bdd044ffb35ac7248827356d358b535fa65d8b0f69ff286410d372f0b7a","observation_id":"f4eb785c-5428-4192-ba51-f706debd1b58","resolution":{"observed_at":"2026-08-11T18:47:46.955579Z","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":"2026-08-11T18:47:46.943804Z","title":"Copod: Copula-based outlier detection","venue":null,"work_id":"15982dae-40da-491b-a32f-1aa1217e2c51","year":2020},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.469989Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:a2258215a0d88f995d247a6d5566caf69d18636e733af87b09cf1a82c57ef214","observation_id":"ca4e51a9-d38d-411f-9492-f7d2a515b44f","resolution":{"observed_at":"2026-08-11T18:47:46.946858Z","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":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-08-10T05:21:27.485481Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-08-11T18:47:46.473276Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.473276Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:fd6937f02cd513eda4612ff942cdd7fbe466793991d27c6b37315e2361941492","observation_id":"0148eb54-2e66-40b0-b0f3-532881e11e17","resolution":{"observed_at":"2026-08-11T18:47:46.473276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-11T18:47:46.476703Z","title":"Denoisin g diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.476703Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:0534ab76d94155fb9c953be3198ba8aeef4a723c2463db711541ca01455b56e9","observation_id":"7a72bd70-eb6e-4242-9e9b-8b410207ebe7","resolution":{"observed_at":"2026-08-11T18:47:46.476703Z","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":"2026-08-11T18:47:46.935637Z","title":"Semi-supervised anomaly detection through self-supervised learning and fe ature reﬁnement","venue":null,"work_id":"5604a47f-ae31-483a-850c-ff0c99f8d202","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.479962Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:51101061966258634bed48a57ff190639fc4cfc824ed15d40b70ed74c432fa3d","observation_id":"59cd0a3c-f72a-4702-af5b-c3434b34a65e","resolution":{"observed_at":"2026-08-11T18:47:46.938645Z","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":"2026-08-11T18:47:46.927063Z","title":"Bagging-randomminer: A one-class classiﬁer for ﬁle access-based masquerade detec tion","venue":null,"work_id":"b981ac24-b566-44c8-811f-52f8a8217423","year":2019},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.483188Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:134c4205fdfcd9debbfc1b7e37d5816d4a74681d9b1dec7c88ebde7e8b0de4fb","observation_id":"1022c596-e51b-4fcf-ba83-65b7b879a9c5","resolution":{"observed_at":"2026-08-11T18:47:46.929757Z","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":"2026-08-11T18:47:46.919517Z","title":"Deep learning for anomaly detection: A review","venue":null,"work_id":"0003968e-506f-4371-a477-57b7851c082e","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.486150Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:3a707cbfa8e9145b0735ac67eb2ee9467d69ef76117a6e748899463b1fbee511","observation_id":"63defaa5-5058-4b7a-b91a-35023dbf0463","resolution":{"observed_at":"2026-08-11T18:47:46.922169Z","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":"2026-08-11T18:47:46.912073Z","title":"A unifying review of deep and shallow anomaly detection","venue":null,"work_id":"a8a5c6a7-fd35-426b-a805-a89a26ecb72f","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.489078Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:e3145b126c855090b994958e89b1adf9040835b32324a91c68cc632cb25932bb","observation_id":"91a37eef-ea1e-4a42-b820-ad00a82721c8","resolution":{"observed_at":"2026-08-11T18:47:46.914724Z","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":"2026-08-11T18:47:46.903545Z","title":"V andermeulen, Nico Görnitz, et al","venue":null,"work_id":"3f81c7e4-8367-49b7-9938-9b2b7ebd050d","year":2018},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.491910Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:737eec9051c49d653ea252e5d324d182672f4ba28279fafe5b62b57b210126d1","observation_id":"eabcd0c2-a692-45b2-80c1-ef13c02740b8","resolution":{"observed_at":"2026-08-11T18:47:46.906526Z","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":"2026-08-11T18:47:46.895172Z","title":"Deep autoencoding gau ssian mixture model for un- supervised anomaly detection","venue":null,"work_id":"bf40f641-17ba-40a9-87c6-9e54850cab81","year":2018},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.494853Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:b93954fe5b4011d93ae3ddbdc61b049224784147d6de41629e5954398dcd2089","observation_id":"39a17fde-9ecf-4165-bc20-fe7e3bb6a30d","resolution":{"observed_at":"2026-08-11T18:47:46.898281Z","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":"2026-08-11T18:47:46.886564Z","title":"Contrastive learning for semi-supervised anomaly detection","venue":null,"work_id":"520f739b-2ba8-4ce7-a41f-424a64b4c34d","year":2022},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.497634Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:a8382156675ddd7409939798df964847c92856acb477d533532780ec4f8bda84","observation_id":"aa9106ee-e500-4bd7-b01f-eb8dd911887b","resolution":{"observed_at":"2026-08-11T18:47:46.889889Z","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":"2026-08-11T18:47:46.878285Z","title":"Anomaly detection wi th robust deep autoencoders","venue":null,"work_id":"d106f0cc-d458-44bc-9461-81bfa452bb18","year":2017},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.500127Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:3ebbc95060411727e7ed3975585b35f82dd4c2140b76adb111da97f9abf8b28c","observation_id":"8bdc9839-13cb-43ee-8c92-9bfa9ce7302a","resolution":{"observed_at":"2026-08-11T18:47:46.881231Z","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":"2026-08-11T18:47:46.869939Z","title":"Drocc : Deep robust one-class classiﬁca- tion","venue":null,"work_id":"d2b3ea54-0544-47f1-a564-8109caec5cfc","year":2020},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.502526Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:f4f580ed3700a75fa9731ddab86814664531872e20788b911259002e4acf9b15","observation_id":"11216084-d9f1-432d-9f9d-f1d0fbb8e939","resolution":{"observed_at":"2026-08-11T18:47:46.872992Z","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":"2026-08-11T18:47:46.861342Z","title":"Classiﬁcation-based a nomaly detection for general data","venue":null,"work_id":"df83692f-735e-48e4-83b7-fc671467f4f3","year":2020},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.504941Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:baf4032eebb2543789418facbafe1d8aa4b63f3f0b06db62b390237d7817cc05","observation_id":"f4f25b79-363f-4d20-8c6b-c9136ddf48e6","resolution":{"observed_at":"2026-08-11T18:47:46.864406Z","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":"10.1609/aaai.v36i6.20629","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:47:46.588834Z","title":"Lunar: Unifying local outlier detection methods via graph neural networks","venue":null,"work_id":"dab2bf1a-d4bb-4227-890d-c2aa9f40f612","year":2022},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.507431Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:dade9a0025349e28fb92236f45d96705917c7346f03138dc3998ebf7b056304e","observation_id":"783bffad-8920-4465-afd1-cf3a8061a631","resolution":{"observed_at":"2026-08-11T18:47:46.593816Z","resolver_source":"doi","status":"verified_exact"},"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":"2110.14613","last_updated":"2021-10-27T17:34:40Z","snapshot_observed_at":"2026-08-16T17:45:59.751216Z","submitted_at":"2021-10-27T17:34:40Z","title":"International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines","version":1},"cited_work":{"arxiv_id":"2110.14613","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.14613","snapshot_observed_at":"2026-08-11T18:47:46.706847Z","title":"International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines","venue":"cs.CV","work_id":"91280196-19b8-4ec6-96c6-faeb5bdbf864","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.510153Z"},"links":{"cited_paper":"/paper/2110.14613","citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:76dcecfaa922ab18134b19501b2c9d87f539a7e5811d9055ac177d13ec215c5f","observation_id":"6d84837c-f170-4305-88f4-8e67ef2f80d9","resolution":{"observed_at":"2026-08-11T18:47:46.710417Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-11T18:47:46.852889Z","title":"Ordisco: Effective and efﬁcient usage of incremental unlabeled data for semi-supervised continual learn- ing","venue":null,"work_id":"affb514d-05d0-4f7d-b31d-88a8d4229c54","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.512893Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:e4ed67f7721549a65cea1e7eb95a9068a819294e6775a6f1cd6a8ce88e03dabb","observation_id":"4dfac1fe-9b22-4b0a-bc0d-6132b3321209","resolution":{"observed_at":"2026-08-11T18:47:46.856046Z","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":"2026-08-11T18:47:46.844558Z","title":"V ariational autoencoder ba sed anomaly detection using recon- struction probability","venue":null,"work_id":"5bd58b2e-183f-40da-8dfd-05384fb99b86","year":2015},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.515889Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:4e6d2447a399d41591d3f9e46387017f1919457378363f2997b8c6adf419f39e","observation_id":"699f8c22-938a-48f4-a57f-66684a880ff9","resolution":{"observed_at":"2026-08-11T18:47:46.847613Z","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":"2026-08-11T18:47:46.835868Z","title":"Anomaly detection us ing autoencoders with nonlinear dimensionality reduction","venue":null,"work_id":"e27c9bfb-c44a-42ed-9c3f-3aa396ff3cd6","year":2014},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.518889Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:8c431961ee20c2b9d2130b379a07d0d4fb2854b83e4147c5da5a0da3e40c5068","observation_id":"60998625-e21a-40d6-b0f2-3685dd5c531c","resolution":{"observed_at":"2026-08-11T18:47:46.838961Z","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":"2026-08-11T18:47:46.827347Z","title":"Learning from privil eged information for anomaly detec- tion","venue":null,"work_id":"aa1b6249-16dd-4fe9-b4c0-f70db37e798a","year":2015},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.521766Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:4ba5612252223a7d48165faa4ed02ab08c5e838047288022328a1c6efa4e33af","observation_id":"999290a9-535a-4eea-ae36-766d4b83cead","resolution":{"observed_at":"2026-08-11T18:47:46.830527Z","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":"2026-08-11T18:47:46.819511Z","title":"Generative adversarial active learning for unsupervised outlier dete ction","venue":null,"work_id":"865d9126-fc9b-4dea-a5f4-6b3500e98019","year":2010},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.524617Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:49b403aae4e642806e609307973f177e38a0a6757127250cb83e4fde159dd6ca","observation_id":"240d42e5-5282-4de2-8647-719a1b15897f","resolution":{"observed_at":"2026-08-11T18:47:46.822391Z","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":"2206.04593","last_updated":"2023-06-17T22:51:22Z","snapshot_observed_at":"2026-08-16T16:54:08.118236Z","submitted_at":"2022-06-09T16:18:02Z","title":"Exact solutions for time-dependent complex symmetric potential well","version":2},"cited_work":{"arxiv_id":"2206.04593","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.04593","snapshot_observed_at":"2026-08-11T18:47:46.695276Z","title":"Exact solutions for time-dependent complex symmetric potential well","venue":"quant-ph","work_id":"3986904d-ea09-4b87-bbec-405424e4f19c","year":2022},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.527414Z"},"links":{"cited_paper":"/paper/2206.04593","citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:52b6bd926bbdb72c174eff8dea97e0cfe3f1e2a282023897f6a1fab610177f1a","observation_id":"e3da80dd-c87b-439c-85dc-3ea92a1b7d57","resolution":{"observed_at":"2026-08-11T18:47:46.698355Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-08-11T18:47:46.811408Z","title":"Diffusion models for anomaly detection and repair","venue":null,"work_id":"91d38d64-07f1-4117-a445-5549755f5e08","year":2023},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.530486Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:e1cb3591c2de7253e5b2ace7e6f0d6ed770b80009c57ee847a6914be0ebbcc22","observation_id":"86d0aded-5f3f-4ff2-bafb-33fdcbd6ccca","resolution":{"observed_at":"2026-08-11T18:47:46.814383Z","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":"2026-08-11T18:47:46.803384Z","title":"Anoddp m: Anomaly detection with denois- ing diffusion probabilistic models","venue":null,"work_id":"3d75e95e-fb06-4c6b-8801-8375bb4b35a2","year":2022},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.533275Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:1ce2f7474063676abc33ae089e224da540391cc567c178ee192639e048d66ba8","observation_id":"3a5f21eb-65f6-4537-bc65-2cc5631d2e94","resolution":{"observed_at":"2026-08-11T18:47:46.806358Z","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":"2026-08-11T18:47:46.794692Z","title":"Tim e-series anomaly detection using ddpm-based reconstruction","venue":null,"work_id":"54df09d9-7973-4ce2-b4bc-9443d83b1784","year":2023},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.536168Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:d29b622fd102f46e57d55009821e60f4b8ebe412f11c7cee74f6e5705cf233ce","observation_id":"42a85c0c-14f7-4412-b76b-9ac838867c68","resolution":{"observed_at":"2026-08-11T18:47:46.797770Z","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":"2303.06789","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:47:46.681969Z","title":"Checkerboard di ffusion models for anomaly detec- tion via image in-painting","venue":null,"work_id":"e773289a-3546-4d1d-92ce-0763090b13a9","year":2023},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.538995Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:ba2d52e5549327f21a017c097a09dac73b909860e4e7b5a893e380d6f9f9f0ef","observation_id":"a7238767-d4a2-4588-8dbb-fbca9e60c938","resolution":{"observed_at":"2026-08-11T18:47:46.686985Z","resolver_source":"raw_fallback","status":"verified_exact"},"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":"2026-08-11T18:47:46.786203Z","title":"Deep generative image models using a laplacian pyram id of adversarial networks","venue":null,"work_id":"14b846a0-df2d-4beb-b7a3-292678bc435c","year":2015},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.542012Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:ec256ab3343317ac8d05f1dca50b740215dc906b87667a4e723eb295daedee70","observation_id":"f8d4db18-4de4-4a19-953d-4cc1ee2710db","resolution":{"observed_at":"2026-08-11T18:47:46.789265Z","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":"2026-08-11T18:47:46.777243Z","title":"Denoising di ffusion probabilistic models","venue":null,"work_id":"a3bf652b-a84b-46ed-8646-295a4bc8f524","year":2020},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.544852Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:47086a1682a4597a53415476dab394588baf37d59a5476e578231b5923e4af22","observation_id":"1bbd29d6-ba17-4079-aee3-faff09e48883","resolution":{"observed_at":"2026-08-11T18:47:46.780495Z","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":"2026-08-11T18:47:46.768072Z","title":"U- net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":"efcc9520-0f2b-44f9-8b38-1bedbcd48cee","year":2015},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.547714Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:701659d13550baaab03d5bc0fbec4f6920399d16a9055b6064bfa0c1544ee422","observation_id":"a67ccf6b-16f6-40a7-b3d0-a219faa6eb18","resolution":{"observed_at":"2026-08-11T18:47:46.771246Z","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":"2026-08-11T18:47:46.759268Z","title":"Dis- criminative unsupervised feature learning with exemplar c onvolutional neural networks","venue":null,"work_id":"1abb8773-93e1-4d58-96d9-95391a14e87b","year":2020},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.550553Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:e9dae8ffa83356cb6e405cbeca8be72c3f383a3359c7b386f4a2ba5e8113ab0a","observation_id":"970fd6ac-3e31-4721-b364-697b578cc0ce","resolution":{"observed_at":"2026-08-11T18:47:46.762542Z","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":"2103.05420","last_updated":"2021-03-09T13:50:22Z","snapshot_observed_at":"2026-08-16T18:40:11.776698Z","submitted_at":"2021-03-09T13:50:22Z","title":"Enhancement of the flow of vibrated grains through narrow apertures by addition of small particles","version":1},"cited_work":{"arxiv_id":"2103.05420","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.05420","snapshot_observed_at":"2026-08-11T18:47:46.612835Z","title":"Enhancement of the flow of vibrated grains through narrow apertures by addition of small particles","venue":"cond-mat.soft","work_id":"262b950b-9c15-4230-a731-0408a85a030e","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.553401Z"},"links":{"cited_paper":"/paper/2103.05420","citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:4a22a876c030f4fbdb65f53febbacfb88c0b141f2f2b115a0e7ed0648d356b0d","observation_id":"87a406ae-6bce-43a9-ae1a-bc124b499503","resolution":{"observed_at":"2026-08-11T18:47:46.616412Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-08-11T18:47:46.750286Z","title":"Auto-encoding variational b ayes","venue":null,"work_id":"eaca6c71-9bf6-4dfd-a65d-849e7e38df82","year":2013},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.556639Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:e4d9dcd91e494ca2383ff243ff140b6b57f56d558dbbbd5a23986995b6480973","observation_id":"92b9708a-430b-4b1d-a890-5df06f6bfd3f","resolution":{"observed_at":"2026-08-11T18:47:46.753705Z","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":"2026-08-11T18:47:46.741630Z","title":"Improved deno ising diffusion probabilistic mod- els","venue":null,"work_id":"0ad0b524-26c2-4b86-be41-e2d5fb72e1c7","year":2021},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.559502Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:9e8a6c85eaba1512613881768ce5458085951c2e1a524d3050cde70069eea02c","observation_id":"2f7b0bbd-f755-4073-ad16-3367b98a1e2c","resolution":{"observed_at":"2026-08-11T18:47:46.744650Z","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":"2026-08-11T18:47:46.732395Z","title":"Diad: A diffusion-based fr amework for multi-class anomaly detection, 2023","venue":null,"work_id":"7b5e4338-f607-4062-ae90-8cc172884c62","year":2023},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.562361Z"},"links":{"citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:65b68cf088e8559c2895f4636fb58ff15a669e9f7ddb2cf2a91f69342f4fdfe3","observation_id":"cf49160f-ba64-415e-96e7-2516e968f117","resolution":{"observed_at":"2026-08-11T18:47:46.735771Z","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":"2026-08-11T18:47:46.565365Z","title":"Ablation1.png","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T18:47:46.565365Z"},"links":{"cited_paper":"/paper/2305.18593","citing_paper":"/paper/2412.07539"},"observation_digest":"sha256:b70cb06508e2d1f91975dc7f7c6e4c4beff0014bffc2231f2290bc484e4f327a","observation_id":"88065759-2768-4a03-90bd-062082a6abe7","resolution":{"observed_at":"2026-08-11T18:47:46.565365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.07539","last_updated":"2024-12-10T14:17:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T14:36:36.366456Z","submitted_at":"2024-12-10T14:17:23Z","title":"Anomaly detection using Diffusion-based methods"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":3,"verified_exact":3,"verified_fuzzy":28},"total_outbound_references":36},"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 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.07539."}