{"as_of":"2026-08-17T20:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b99a3a9ae4b6ba9a1e7ec036abb787a41b73b1eb895e639bd87ee1be59129ad9","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T19:53:42.921277Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"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/2605.26468/citation-record","integrity":"/paper/2605.26468/integrity","json":"/paper/2605.26468/citation-record.json","paper":"/paper/2605.26468"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Wafer level stress: Enabling zero defect quality for automotive microcontrollers without package burn-in,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:7afa5c5b7fe58ddaa73be55fa6bff2afc054b530ae370ae89f0d6e1df4aa669c","observation_id":"b8b61830-5b01-4d89-8bce-99b531dcfa40","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Silent data corruption: Advancing detection, diagnosis, and mitigation strategies,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:dc4ea8bd7a37502a16b9739f2da240027ead1a92b47e6a5eee9c57a73aeea93f","observation_id":"6e1a78e6-c929-40cb-9f3e-5df044c7dcfe","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:1830f62cac45aaf7b8c5004a6e927c72e2e815b70c34980231652162e05329ab","observation_id":"8246d7d5-2273-49e7-bfaa-8fe70bb26527","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:d5aa177ad8ec2a3298ab88215d9d1eeb830be8ffe82dd8c0a11415fc8ead4645","observation_id":"2fb404ca-0079-4ae1-9b34-989037faa87f","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08730","last_updated":"2025-05-17T03:30:17Z","snapshot_observed_at":"2026-08-16T15:47:57.454251Z","submitted_at":"2023-03-15T16:14:06Z","title":"DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection","version":4},"cited_work":{"arxiv_id":"2303.08730","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.08730","snapshot_observed_at":"2026-06-29T19:53:55.256209Z","title":"DiffusionAD: Denoising diffusion for anomaly detection,","venue":null,"work_id":"7f4be92f-3845-4acd-bdef-63916e6f01e4","year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"cited_paper":"/paper/2303.08730","citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:4ccd04e015314d19db71781c05998151aa2426f1c64304856337bf3247c4df2e","observation_id":"c779614d-e467-482c-8c4a-aa1102531350","resolution":{"observed_at":"2026-06-29T19:53:55.257716Z","resolver_source":"arxiv_id","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Tabddpm: modelling tabular data with diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:768511a36205a4c5376251ede38a56cce50d603e80944826bbd0b2c42eb5414f","observation_id":"81af4185-133a-4927-a5df-69678a0415f2","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Scalable diffusion models with transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:1c82d2ba6a6280a82def249e618e114b5def5cf5fdd996382962edac6dec2d7e","observation_id":"c5c116ff-cdbe-4ba9-a799-7dc3e8873c50","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:ec601141a444090d9e0566ddf276d822d8c82f35e99853526167593e71f95f4a","observation_id":"298e7c80-7053-4118-85d9-6772b9134b8b","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Advanced outlier detection using unsupervised learning for screening potential customer returns,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:6980562a483c057cc46cab6fa610e7359dd967dca9eec4657e3a490a54120e4f","observation_id":"1992de1d-7787-43ea-9f9c-4f17b24c9073","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Isolation forest,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:8bfe2df30482f851496fcfa1a606c1be0262bbb78cecd36c9ed8cf2d6adaf8a9","observation_id":"ca7107da-6742-42fa-89bd-ee2e0e578177","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Estimating the support of a high-dimensional distribution,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:075f641439ca361b03204b92f078cd794a695d350753f13c855882c34db56d62","observation_id":"f097ab1e-20e1-46ec-98e8-9e81bf0775eb","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Equipment anomaly detection for semiconductor manufacturing by exploiting unsupervised learning from sensory data,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:2b5f0b83e7d20953bf19576966932d12c19cdcf0d4d18847e9a1989926381775","observation_id":"0c997cf9-4f0a-4199-8755-b444ffcdea49","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Generative pre-training of time-series data for unsupervised fault detection in semiconductor manufacturing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:97b40c61868a06484ddc3ec931d7368f79ed6eef0446ac75f3503320d0356855","observation_id":"c213f4b4-3278-4cae-8874-9dfe02d725c8","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Wafer map fault pattern classifica- tion and image retrieval using convolutional neural network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:3c74058a9e54c695d98f320134a0674565c60b24b524758e0d60d76cd07489ad","observation_id":"ab07c85a-5763-4a18-98ff-c0406e86c582","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Input-guidance diffusion model for unknown defect patterns detection in wafer bin map,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:1ea79ac0d2b07008bce3a527f193d36460c56249cf15f7db4f8b50e7c389e9b1","observation_id":"6d7bfa41-7780-4e2a-8fa5-7affeda97d10","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"On diffusion modeling for anomaly detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:207a3b9c99a0e5d8c21f0b476f6f29937215c5077c506aaed0158bb6e1f04b72","observation_id":"c8d89718-b23c-4988-bb71-923950f4346f","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"ImDiffusion: Imputed diffusion models for multivariate time series anomaly detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:351128503c41de793f6377b54434330d08e7df8a636db2890af3c8748fa1a8f9","observation_id":"4e1ed6f8-f45a-4a6a-af3b-6be7754e7f4d","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Anomaly detection with condi- tioned denoising diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:b5173897ec61f61448cee64d190183a9ef2f0d719da2e02dd4ef09871bd295a4","observation_id":"b12ad3b4-b820-4e33-9ad7-8db1c66159e2","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Unsupervised 3d out-of-distribution detection with latent diffu- sion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:e954d848c8b1c94914cc5a5942ca2fcf1f792fc08fb2e95a5465b66dd6e908f1","observation_id":"f8c78b59-e551-4b24-9ccc-782e8d5f8d8a","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"TransFusion: A transparency- based diffusion model for anomaly detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:252f72c5219a858d0a992b2e5e8e9e88c1b77f6fa5c467790485f3e88498f1c1","observation_id":"c5b7c3c2-dd5b-4ab3-bff1-5d6eff47bb2f","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"MVTec AD — a comprehensive real-world dataset for unsupervised anomaly detection,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:84da65b0a90abdd7c040dd4ab38d733a997b75f304f3d98a6f3e0736666ad7f1","observation_id":"73b253bd-5e43-4d20-a3b3-9d8852886552","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:1b067567b985ad6083f92098adef19806d9746bfc241cf24991b69671ef6c8f7","observation_id":"9a32f055-b898-4a1e-940e-4933d520ec68","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:5f5229af1d9dc45d881f67f4e9b8795275baee9112dcdf47bcb35dd044c4b8e8","observation_id":"a8cebb68-9b36-4c15-b02e-d1da91001eed","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Improved denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:fbeaeb9fe18c56fd5565e5a3bc652b5e4f433939092b59ce5a25ee0c9690fc12","observation_id":"7ae880bb-1e70-4d51-a288-987aa158db89","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"8162–8171","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:a8eb6258f61d5fd4d862f93e6d394631a0877190aa8243ef1043927eabd8be96","observation_id":"aeec2709-0f99-4add-aed0-aff819311516","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"The relationship between precision-recall and ROC curves,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:760c349892b68378ba668a2689be41b2aef66bf605f5992b64f23ea1e6b679a3","observation_id":"e82abd02-b5ba-431c-97ff-6d9f99591ffd","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"COPOD: Copula-based outlier detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:81dd94fcb36235bda689dc542ff26f6bdb5dd3f7c4a33afe665c43f982743302","observation_id":"49b40c47-e2b0-4354-9aa1-e6cb4e4be2ef","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"ECOD: Unsupervised outlier detection using empirical cumulative distribution functions,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:723819b49080bf594cfa33867535a7271195b4fdda2c9441ef08bda8bd39e97d","observation_id":"3ed2ce3b-8540-4703-a82c-c945876fba74","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Feature bagging for outlier detection,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:bc47d32ec750996c9d4be85b15491b27cf4cec19492837c74ba91c9764cc1ec6","observation_id":"96564072-b43a-4bb5-981f-a40353f2f963","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"HBOS: A fast unsupervised anomaly detection algorithm,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:b191b27cf5a614b0237a7ad0de491d8e39bf600d3f3d8ffd36dc448d23c42992","observation_id":"c6e19df3-c2db-4f89-8d1f-0a890cf5a413","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Efficient algorithms for mining outliers from large data sets,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:8851df51cddd18591332abb2ecfac052371b3ccb378894c3e249c08ca638e84e","observation_id":"e2d65851-0022-45a4-8228-6cd692e80901","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"LODA: Lightweight on-line detector of anomalies,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:75206e0a123b79fbaec1ab939bdb6a6e18ce20a867205f5338199c6faabd1be2","observation_id":"ba74251d-94c6-4565-bdb9-01e9fb3e9b31","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"LOF: Identifying density-based local outliers,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:8841e1e8203531f31ea009df6b2e1ea2fbb1a979163c768da2805e30bdaf04bf","observation_id":"c9e05897-0e00-4c8e-9461-c4f00ed4a690","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:feb97eba9e24ed8d2a0503655c9fcd9b1ec09d02c4030b73bf33b2da90b2b886","observation_id":"6b0ff077-36ad-4130-9c81-2333d976533a","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:965fe4416ad6b1d14142d40ae3913fa7037c2e4fa1f9f50ab938477467073246","observation_id":"053bdba7-f43e-4dd4-b6fa-2bd8bf705919","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"DROCC: Deep robust one-class classification,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:274fd5212332a1718eb47c5d86556dcf60f774844bb34dd4cd07f0b1c093b72b","observation_id":"c6671ba5-3717-4350-835b-d8a002ea4815","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Classification-based anomaly detection for general data,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:ff66e927ecedfa0be7071b4d0b0184fb905eaa49b03e7f7563dd64d876f1559c","observation_id":"ee94400a-0012-4636-bb24-51079968853e","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Anomaly detection for tabular data with internal contrastive learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:3ce064de1199b692d016450b0d4107a10f76bfe8b861ae6ea41a02d76ef67a06","observation_id":"bdfac4c8-085c-4255-8d81-2c6422af27cd","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Variational inference with normalizing flows,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:d6c41c540ef5dc60275df5e473e94765b2cc6db385061da06d3ea881a51add3a","observation_id":"909bd74c-d2e2-4752-b340-56eb9b9d27e6","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"GANomaly: Semi- supervised anomaly detection via adversarial training,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:fb91a6d4a7d9e21495bb7355a4a71fb2ad4b409e5de5c87290ddacdf4c660c17","observation_id":"93210de4-8b52-4806-9565-e7bc9bd75ead","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:eee4622ab282a71efcdb7dfd14934cdee280031e067ecd8ce6e60e8967682c05","observation_id":"15508129-1f54-4940-b27a-d60afedf6594","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:53:42.921277Z","title":"PyOD: A python toolbox for scalable outlier detection,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:224fc8c88415e6adb0290cd3b1de4f212e5adb1d96cbfaa27fbe48838c9c9781","observation_id":"63521cec-6b25-41b8-9f2c-c3152855a81a","resolution":{"observed_at":"2026-06-29T19:53:42.921277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":"1711.05101","doi":"10.1137/1.9781611972825.47","metadata_source":"pith","pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Decoupled Weight Decay Regularization","venue":"cs.LG","work_id":"07ef7360-d385-4033-83f7-8384a6325204","year":2017},"citing_paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T19:53:42.921277Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2605.26468"},"observation_digest":"sha256:d070a968be59baf8459e7fc7605b64564f444773aa7fb5eaacdbd2e6f6afb9f0","observation_id":"f62c3733-5a35-418e-8701-164ad652ee40","resolution":{"observed_at":"2026-06-29T19:53:55.255103Z","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"}}],"paper":{"arxiv_id":"2605.26468","last_updated":"2026-05-26T02:24:44Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T07:56:59.590073Z","submitted_at":"2026-05-26T02:24:44Z","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":43},"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 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2605.26468."}