{"as_of":"2026-08-05T16:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:366b0cc4abfc6b1e5e08e5d3ea443fedf32f40be50358b70ea6dabc3f69542d7","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T09:14:17.683170Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2605.30046/citation-record","integrity":"/paper/2605.30046/integrity","json":"/paper/2605.30046/citation-record.json","paper":"/paper/2605.30046"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection: A survey.ACM computing surveys (CSUR), 41(3):1–58, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:a3a15382d2254d9ab79d99409d0d3816ecea3a9f626417613fac96b7c563735e","observation_id":"0e908aac-9f38-440a-9419-cd6908b0d44f","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"A unifying review of deep and shallow anomaly detection.Proceedings of the IEEE, 109(5):756–795, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:f4f32857352e7e2d98e3ca57d625a6178f6f56af7702141f5c50c5025a5d5490","observation_id":"66638b27-9d19-4755-842c-118c1ea5f9a0","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Deep learning for anomaly detection: A review.ACM Computing Surveys (CSUR), 54(2):1–38, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:b34453079702d635d76ce77f87a6b3051c73fa52aee72441ca8e2ea368828988","observation_id":"4fe93044-0258-41be-a04b-fbd9f8bcbf23","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Sensor fault and patient anomaly detection and classification in medical wireless sensor networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:7f506a829c508cdb9de3a630e34fc35d07905f4782e30a83b83e9556c1ceb0e9","observation_id":"a2f6ace4-df95-48e2-8a9b-173c249fee25","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection in medical wireless sensor networks using machine learning algorithms.Procedia Computer Science, 70:325–333, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:9d947a296387638dd102bda7b083fc6c1c656b06aa62bbd15b179e08bd4a41be","observation_id":"1b5c5543-7631-4771-8510-4d012d296883","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"A survey of anomaly detection techniques in financial domain.Future Generation Computer Systems, 55:278–288, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:19afdb8fdbf6ef45489673b54e1b2e3039622a261096120f29b92b4d1caf8cf5","observation_id":"21e0fb32-b2f0-47ac-8cb6-323f733fae00","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection approaches for semiconductor manufacturing.Procedia Manufacturing, 11:2018–2024, 2017","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:64443246414a9361136dfdd448d448d5373774cd3d8726ff73ad40dfea570c8c","observation_id":"a051e33f-110b-47a6-b558-e9ee15d4b4eb","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Challenges for unsupervised anomaly detection in particle physics.Journal of High Energy Physics, 2022(3):66, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:db795018d3f8bf26070d9df69b0cd8a1b00d6d3e3eaa328e0d4b9a3b2f4addf6","observation_id":"16619a3d-ff12-4aed-adea-c64bef6dd117","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis 12 for space systems","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:646d9195ca8f68fd6ec889f24dbecb56366628a572821d15bccfbcd662ffe2c3","observation_id":"c22610fd-20d6-4c3d-ad68-7ad9bd03280c","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection methods for categorical data: A review.ACM Computing Surveys (CSUR), 52(2):1–35, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:f08852f2227594bdd8f3d7a04e2b0c36d50a7d39cf2d710b07eea23c85f16e82","observation_id":"e7de8a38-ccaf-48a8-88a6-f1ef029762f1","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Adbench: Anomaly detection benchmark.Advances in Neural Information Processing Systems, 35:32142–32159, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:c5e9a90c3df43edb62eebe6b2c9491a30da58ca271730fa01842c7bbd695d315","observation_id":"41fdbd91-a4b0-4789-9b78-b7a79adfa6df","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Nlp- adbench: Nlp anomaly detection benchmark.Findings of the Association for Computational Linguistics: EMNLP 2025, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:c328507cc6e6e81d657ae285b0dac5c089f223ae21ab9c944cb04fda89ce27f1","observation_id":"202cb65f-01c9-4a48-b6d0-0849768af6a3","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Deep unsuper- vised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:b013210289c4a2ad2db79ecde67591e22bd80cd3fd1dba0191b3cde90497b8c0","observation_id":"8c95c68b-9c83-4ab6-ab5f-97af43f5aae1","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:889a712a8c95dfa5c539848e9fee9968a548f74f534e4a9fca939f0d6c32d135","observation_id":"3e655977-be98-4e7d-83c5-240aa712c337","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Simple and effective masked diffusion language models.Advances in Neural Information Processing Systems, 37:130136–130184, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:9caf9ffb11986ad6a8399727a205a75e5b8fb2ccc2bf7530cc8a9e4448b167e0","observation_id":"8416e2df-c020-44d3-9571-5db45489351c","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Structured denoising diffusion models in discrete state-spaces.Advances in Neural Information Processing Systems, 34:17981–17993, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:bf4c4a853b17eb5539a2b2efb8c82c839037f58330963a637ca3e3c95ed372ca","observation_id":"19d641a3-5714-44a8-ac04-92c5d3ac787c","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Masked diffusion models are secretly time-agnostic masked models and exploit inaccurate categorical sampling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:d79b646914c831edd4bf1cf7f8f2cd5ae72a27a27450d16bd19937bfc475f47a","observation_id":"777629f9-33da-42f3-b5c4-f3814a6c99ed","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Dif- fusion beats autoregressive in data-constrained settings","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:228fd19f2f17e9a7d8e35cd8813f58c6f447cbf8262e5477f2fab3cb64173320","observation_id":"0ae4429b-d338-403a-ba71-bfbc35322950","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2503.09790","doi":"10.48550/arxiv.2503.09790","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Con- strained discrete diffusion","venue":"ArXiv.org","work_id":"99a69656-6fdb-4abd-9901-293a4bcd5d30","year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:d3276f463115d04c2e92878fb2eeaafc4813826da5ef8c8bd61f6f9e81d56df1","observation_id":"1a6f45f6-6873-4652-9b1a-54c670a61799","resolution":{"observed_at":"2026-06-29T14:03:29.956001Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13734","last_updated":"2024-07-18T17:35:32Z","snapshot_observed_at":"2026-08-04T02:06:15.701646Z","submitted_at":"2024-07-18T17:35:32Z","title":"Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review","version":1},"cited_work":{"arxiv_id":"2407.13734","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.13734","snapshot_observed_at":"2026-07-05T17:51:14.851401Z","title":"Understanding reinforcement learning-based fine-tuning of diffusion models: A tutorial and review","venue":"cs.LG","work_id":"a22cff4a-9d87-46bd-8d73-d48374c743ef","year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"cited_paper":"/paper/2407.13734","citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:c6e40f2e2721ddface7e3506d185c5d3ddf91d43c86321fd497d33c85525e2f1","observation_id":"f1963548-e403-427c-b430-6ed25946aeba","resolution":{"observed_at":"2026-06-29T13:53:29.855029Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-29T09:14:17.683170Z","title":"On diffusion model- ing for anomaly detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:81c1cfcc2717f2b31e92d49eec8054a68c57602f664ae33ed6d1bcecbe490160","observation_id":"a06792fc-1955-4960-ba1c-aa2cd334f1d0","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Efficient algorithms for mining outliers from large data sets.SIGMOD Record, 29(2):427–438, June 2000","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:2683d4f171dd2935a826172dfc010add71c6601b61575295612caa158ac86370","observation_id":"eaf5009e-7bbe-44ee-965b-9873b1837572","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Isolation forest","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:ee8c0b6ff93c2079654bc4c25415d6a3671157c1c92f06a24cef805efb6b3ad5","observation_id":"36a88bc5-7b31-4589-bce2-37aa7154cc80","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:5359b0eaef45ef5500231a804119dcd3b963ecfb9924b5236cf80b9a0f0a155f","observation_id":"29db9991-169c-4bd0-a544-c855df27752f","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Copod: Copula-based outlier detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:c0f671d1ac66ac8680db62d490029a7ad71ba467c2167e91da581402949d199d","observation_id":"ed9812a6-afd6-4d1c-954d-0a297b759e94","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.14056","last_updated":"2022-10-26T04:03:43Z","snapshot_observed_at":"2026-07-06T14:10:12.547986Z","submitted_at":"2022-10-25T14:33:17Z","title":"Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings","version":2},"cited_work":{"arxiv_id":"2210.14056","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.14056","snapshot_observed_at":"2026-06-29T13:53:29.844904Z","title":"Unsupervised anomaly detection for auditing data and impact of categorical encodings.arXiv preprint arXiv:2210.14056, 2022","venue":null,"work_id":"4cfc9a9b-0316-4010-8b61-16dd9d56e810","year":2022},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"cited_paper":"/paper/2210.14056","citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:dd469267db85ed1444b71c25725278f18d07009f411d01b0b10eab930108ee4a","observation_id":"64efe070-5007-463c-94dd-847d9a97c0ad","resolution":{"observed_at":"2026-06-29T13:53:29.846698Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-29T09:14:17.683170Z","title":"MCM: Masked cell modeling for anomaly detection in tabular data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:cc1bdd76a8144dfee890c22dcd4813fd71bfed4e84177742a232194a0ae2cd5c","observation_id":"acfb6676-743c-4931-85d1-4287401a886d","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Beyond individual input for deep anomaly detection on tabular data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:e6968888d947e13a0dbad18abf488f769796822cf8bed910adf275fc455153a8","observation_id":"f26e1a84-0731-4c0f-b17f-638ee64a382b","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection for tabular data with internal contrastive learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:43e3c788e78a93f6de6174b918ae2ed9e5c811e990ee2f402ffe33f27e1b1f88","observation_id":"9d610d44-ea44-46bb-a1fa-d32d499b020c","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Fast and reliable anomaly detection in categorical data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:9acbf7218f98f7b1edcfedbd1b3ce75b0ae032ed39b602849319edf706e56ac1","observation_id":"0d70bc27-1a79-43e1-b0de-68840935d67b","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:644de13d932f093fa67efa3f1a12ed0b69c64726e60347999cd3dd5a61da11f1","observation_id":"088bb506-b6d5-4c86-8495-c59d4a99f51a","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"DRL: Decom- posed representation learning for tabular anomaly detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:3dc3be77a908d22474f95125ce5e64c7e39face67e64c062b03289c2ec2373f9","observation_id":"a163e6a5-f81a-4dec-920a-331c28051f9f","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection using autoencoders with nonlinear dimensionality reduction","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:b7221f805e5f71e839c31dae3ff25f54a3a52822f6075013c35902b2ce01f1e9","observation_id":"bd35b21d-efe3-4522-b70f-a05cb78c6208","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Variational autoencoder based anomaly detection using recon- struction probability.Special lecture on IE, 2(1):1–18, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:b4592142cd1493134618bb97aadb30a6fd42ed3f20ed759e287d90cff70973c2","observation_id":"c2139137-5c43-438c-8040-992592c85a6f","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:c6d5bfd56497f4d4b79643e5706d456b656943a46c70216970ad90c85768924e","observation_id":"5a75d5fc-6cf3-4bb2-ac71-ec004ca7f89d","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Waldstein, Ursula Schmidt-Erfurth, and Georg Langs","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:93a9406d1ff026c5650932ae6a156cc95b51a6a3500004a5267c9e4fa6ce8178","observation_id":"086c0867-be37-4eac-bc97-be5f3e736d77","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Ganomaly: Semi-supervised anomaly detection via adversarial training","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:e67a557aaa52d86bb0da892a1806ff96c9f7e830895e4ca165c56bc8c3741fe9","observation_id":"25a47216-6cf2-4306-8ffa-fbb4c28fd95f","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Same same but differnet: Semi- supervised defect detection with normalizing flows","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:7d786b6d33d4271c90f6fcbb6cb7366e67e3fb01ed2da026e68a6ce86f638f59","observation_id":"8c71162d-c6b5-4a7d-ba1a-9ce708e3ef9d","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11430","last_updated":"2025-02-27T02:05:55Z","snapshot_observed_at":"2026-08-03T17:36:29.701370Z","submitted_at":"2025-01-20T12:06:54Z","title":"A Survey on Diffusion Models for Anomaly Detection","version":5},"cited_work":{"arxiv_id":"2501.11430","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11430","snapshot_observed_at":"2026-06-29T13:53:29.848553Z","title":"A survey on diffusion models for anomaly detection","venue":null,"work_id":"fbab095e-9fe6-489e-9b8e-65786b6fa13e","year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"cited_paper":"/paper/2501.11430","citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:b2d3d5290eab95adeb9bc9267b7679d6fc4727e2dc8bcfe8607ce4ca64a15739","observation_id":"b6969f32-7bf5-46dc-9368-c161ab7e582e","resolution":{"observed_at":"2026-06-29T13:53:29.850105Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-29T09:14:17.683170Z","title":"Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:5df748550c0cea9b18866c415b1be6a68db9b1d4ea93e365d0fe5e2cc9c351b3","observation_id":"2bd31f25-f5cb-43a8-aeaf-b867b98a9103","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Anomaly detection with conditioned denoising diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:284d9608bcabc1a7efa435b3f65fff2c0a93d92d711c3e015563f85f1203a640","observation_id":"954d1c3f-b215-4ef4-b590-aa404010d514","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Diffusionad: Norm-guided one-step denoising diffusion for anomaly detection.IEEE transactions on pattern analysis and machine intelligence, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:f2abbcaf77031e88b6b15dccedb07851312e1a836ffa4142552c3489acac9a27","observation_id":"e6719fd3-b82b-4a30-bcfd-aa484c4fbd54","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Simplified and generalized masked diffusion for discrete data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:a7f39052f9124e71fafeb64a6806e2df1baf6bf1dc66635af0a97b051c10e410","observation_id":"9c59f25f-428c-4c7b-8c7f-c365e26acffb","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:f43cebf3c3a318b71790c833961468323aa59a7b3760b3104bd73dc9470906db","observation_id":"86f7164f-9af4-4098-8f35-b4b58a3bae57","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Deep one-class classification","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:de26f2ae94fc00ca8982eaa35d9ed471414e7c5648920a8ee1e3db2370f57ffd","observation_id":"148238a8-032f-4ae5-99e5-664316d0dde9","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Classification-based anomaly detection for general data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:25048cf7a91048bbb7576b192ae967e2e9ffc61fe9de9f371bfdc4de3ed39b84","observation_id":"14453dac-9e76-4af8-9dbe-3a987b4b5c94","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"DATE: Detecting anomalies in text via self-supervision of transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:e360b87f8b1d6c91e3fd5991b097ed55be9923487a3b4293cd530e1cb48b10cb","observation_id":"d8a6e3b1-8288-4a8f-ba3c-650a57d51f74","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Few-shot anomaly detection in text with deviation learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:1ec2629b198c1c5f71f02cee0fd96acc91bf1a7d2791007bb260a10240ade6c7","observation_id":"efa2b0f0-3645-4bee-bd3f-b89149cbf7b2","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"BERT: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:31d8d69d945f9cdab504e2d71367613389c15c8678ded23ce197fd3f911585d7","observation_id":"f6e499ae-6ed7-4684-89dc-ff00a043c75f","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"New embedding models and api updates","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:7a82c9f3ef3c7fa5f012c41ebfc8c593f74afef77e6bbbc0ee459a900bd3ec5d","observation_id":"56501c5e-dcae-4a28-9c6f-8702c7c7cb25","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Lof: identifying density-based local outliers","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:2a7b5d06c1ea418d151e2a6625a4fc94312741c1daf737a090d5d0d29951e207","observation_id":"56f6a8b4-fac9-44e6-b9ca-4f052b706892","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Generative adversarial active learning for unsupervised outlier detection.IEEE Transactions on Knowledge and Data Engineering, 32(8):1517–1528, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:9f15ffa58aab18be2ad864fe1e905a38820b12bdd93d6f2e6a8c8fb208c74985","observation_id":"9588f00a-e116-4596-8048-8f520de6a24c","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","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":"2026-06-29T09:14:17.683170Z","title":"Aggarwal.Outlier Analysis","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:f7384c9a740ce4c68bfd287f4a6c32afddffab5fbb001f658a65837b3f1e8745","observation_id":"5fded65d-5bfc-4e1a-8d40-6d9cefa289c9","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":"1312.6114","doi":"10.2139/ssrn.4269703","metadata_source":"pith","pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Auto-Encoding Variational Bayes","venue":"stat.ML","work_id":"97d95295-30e1-42b4-bbf6-85f0fa4edb44","year":2013},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:0646eee364b5b07841be19c360c6a95188faff9cfbf479aa4786927fdcdb7dc1","observation_id":"e1ecf357-5ce4-4402-8d92-8617de189c95","resolution":{"observed_at":"2026-06-29T13:53:29.852412Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-29T09:14:17.683170Z","title":"Lunar: Unifying local outlier detection methods via graph neural networks.Proceedings of the AAAI Conference on Artificial Intelligence, 36(6):6737–6745, Jun","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-29T09:14:17.683170Z"},"links":{"citing_paper":"/paper/2605.30046"},"observation_digest":"sha256:a9fafdaac80bed15a0c7f3be4794c4a23a555f22e904e8a74aed250d2c04887c","observation_id":"66758c62-5410-4d21-8441-91fd8ff17ad8","resolution":{"observed_at":"2026-06-29T09:14:17.683170Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.30046","last_updated":"2026-05-28T14:59:17Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T10:26:56.468021Z","submitted_at":"2026-05-28T14:59:17Z","title":"Masked Diffusion Modeling for Anomaly Detection"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":49,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":55},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2605.30046."}