{"as_of":"2026-08-07T10:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dc64f2234e065c981535f90a35eb9394f48f20259641fce9ecdaafdb56b5ab18","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T19:56:10.982333Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T16:37:40.632230Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.11528","snapshot_observed_at":"2026-08-03T16:37:40.632230Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.12628","last_updated":"2026-07-10T05:49:59Z","snapshot_observed_at":"2026-08-07T09:37:37.370099Z","submitted_at":"2025-12-14T10:22:03Z","title":"Integrating Fourier Neural Operator with Diffusion Model for Autoregressive Predictions of Three-dimensional Turbulence","version":3},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-03T16:37:40.632230Z"},"links":{"cited_paper":"/paper/2508.11528","citing_paper":"/paper/2512.12628"},"observation_digest":"sha256:6e97f000ea510ad34a9863d6437914147ae60119d72d0925d095792c16528bfd","observation_id":"aadad655-aa67-49a1-9415-866568fb7f75","resolution":{"observed_at":"2026-08-03T16:37:40.632230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.11528/citation-record","integrity":"/paper/2508.11528/integrity","json":"/paper/2508.11528/citation-record.json","paper":"/paper/2508.11528"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.360050Z","title":"Janot, M.G., Brunot, M.: Data set and reference models of emps","venue":null,"work_id":"18cb8b37-44ac-495f-a774-472b4b14ee00","year":2019},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.898800Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:01784c3521c927e63bf0a0a80f22cdc417433267b63d6522eb6d4b8a8fe19729","observation_id":"dba48e7b-50f6-4dec-a3ad-bb8914d4b3ed","resolution":{"observed_at":"2026-08-05T19:56:11.363606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-05T19:56:10.901979Z","title":"arXiv preprint arXiv:2403.14404 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.901979Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:76f877a42b714e3ca2eeba4b9bd2bf75a1167e0ccece494cee9c4559dd852b30","observation_id":"62421ddc-4f76-4b69-9ab7-ca6dbbbd1ba7","resolution":{"observed_at":"2026-08-05T19:56:10.901979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.350707Z","title":null,"venue":null,"work_id":"32baf6d8-c19a-4c9d-8741-ec80845db9ff","year":2022},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.905106Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:f462f55a6405f3d23238f07ed47e44bef244abac36ed1884a3b47d6c56c3507d","observation_id":"ee9de46a-e7bf-4676-a04d-238670fdb9eb","resolution":{"observed_at":"2026-08-05T19:56:11.353549Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.341431Z","title":"Journal of the American statistical Association 112(518), 859–877 (2017)","venue":null,"work_id":"710323d7-558e-47e1-b028-19b6522e4b47","year":2017},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.908020Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:563acb6b4c47503fa1d229022ea1af95e1a1c4a896f586ea65111009dc21e89f","observation_id":"0c991da6-7151-4146-beff-9a13cf690787","resolution":{"observed_at":"2026-08-05T19:56:11.344633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.00754","last_updated":"2023-11-14T06:55:30Z","snapshot_observed_at":"2026-08-04T17:21:53.323607Z","submitted_at":"2023-07-03T04:57:40Z","title":"ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.00754","snapshot_observed_at":"2026-08-05T19:56:10.910977Z","title":"arXiv preprint arXiv:2307.00754 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.910977Z"},"links":{"cited_paper":"/paper/2307.00754","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:0f9889544516d449dd4c8f7fcef006c97aa6857df9b45a3ec914aa2c938df36d","observation_id":"921d7759-ac82-4aef-abcc-83401c188f35","resolution":{"observed_at":"2026-08-05T19:56:10.910977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03559","last_updated":"2024-11-01T20:15:18Z","snapshot_observed_at":"2026-07-06T17:25:53.950578Z","submitted_at":"2024-02-05T22:18:16Z","title":"Constrained Synthesis with Projected Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03559","snapshot_observed_at":"2026-08-05T19:56:10.913973Z","title":"arXiv preprint arXiv:2402.03559 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.913973Z"},"links":{"cited_paper":"/paper/2402.03559","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:ceb4593b4d8f9532909ffe8a31ff1d6d7f4e0f1413c536c9e0f2ec3d14fa2887","observation_id":"3d81cbbe-712b-41e6-a83a-a4583da03379","resolution":{"observed_at":"2026-08-05T19:56:10.913973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.331956Z","title":null,"venue":null,"work_id":"17f9022e-05b4-4103-9ee2-bfa23e37c9f7","year":2022},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.917241Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:923e2d90b39592e14eb4ee4bacc8e177336baa391d1e49d20593065399d9a6da","observation_id":"97caee28-2f9c-4130-966c-307bfe95dddd","resolution":{"observed_at":"2026-08-05T19:56:11.334937Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08095","last_updated":"2021-12-07T09:00:04Z","snapshot_observed_at":"2026-07-06T12:08:50.277340Z","submitted_at":"2021-11-15T21:42:14Z","title":"TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08095","snapshot_observed_at":"2026-08-05T19:56:10.919947Z","title":"arXiv preprint arXiv:2111.08095 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.919947Z"},"links":{"cited_paper":"/paper/2111.08095","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:f24be63a37e87c3595ae1190340daea6c97ccf60a75b6b7852c3d403ec382d42","observation_id":"af4d32a8-01ae-43ba-bcdd-6c697605ac9b","resolution":{"observed_at":"2026-08-05T19:56:10.919947Z","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":"2026-08-05T19:56:10.923028Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.923028Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:32cf59f61fa2b20c47ee6f4171dd38f7e75f27139fd5a46e59cb86185ed7c515","observation_id":"e9bf98e4-15ee-4f3e-a67a-c39cd9606368","resolution":{"observed_at":"2026-08-05T19:56:10.923028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.316468Z","title":"In: Asian Conference on Machine Learning","venue":null,"work_id":"15f8f024-d18b-4710-9acf-2f35934e2052","year":2018},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.925538Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:86bea901af69b8b8074d700659e0b318bec1c433273ea0888f2dc4640c61584e","observation_id":"54059f95-cd3d-4e42-ad40-49cf608c568c","resolution":{"observed_at":"2026-08-05T19:56:11.319388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.306343Z","title":"In: 2021 IEEE 17th International Conference on Automation Science and Engi- neering (CASE)","venue":null,"work_id":"60caccf1-0dd8-4cf2-940d-98eb57002c7b","year":2021},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.928405Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:2f41594444b4dc5fd017a816f429717803ba82c59bec92302e97d4c893407fe0","observation_id":"1bc098d9-3eef-4783-bb29-c107b3ec4cc7","resolution":{"observed_at":"2026-08-05T19:56:11.309525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T19:56:10.931649Z","title":"Advances in neural information processing systems33, 6840–6851 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.931649Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:b85d3245c683640c12de1e39f2124b03dab8076e244909abcd1500c22db3226c","observation_id":"ca77a2f0-5372-407c-8218-f31e06a6c9f1","resolution":{"observed_at":"2026-08-05T19:56:10.931649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.291387Z","title":"Scholarpedia1(10), 1563 (2006)","venue":null,"work_id":"2365f26a-cc4a-4482-894f-bbfbf1a0392d","year":2006},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.934440Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:aa803f764b8b1f9a544f82bcfab3accb17565ffbb269ef140a03132807024fa2","observation_id":"60980f66-c998-4996-98a5-1417d11d2241","resolution":{"observed_at":"2026-08-05T19:56:11.294340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.282821Z","title":"In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","venue":null,"work_id":"5eeeabf7-847d-495f-b526-6679b8bdb578","year":2024},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.937061Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:cd67fae731285e703ef51cfe7105c334006456b56604751431d7db32e6da497f","observation_id":"01972616-0200-48a7-b4d0-f29157540c82","resolution":{"observed_at":"2026-08-05T19:56:11.285768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10527","last_updated":"2024-10-20T15:18:03Z","snapshot_observed_at":"2026-07-06T17:04:05.327440Z","submitted_at":"2023-12-16T19:56:10Z","title":"CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems","version":2},"cited_work":{"arxiv_id":"2312.10527","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.10527","snapshot_observed_at":"2026-08-05T19:56:11.117235Z","title":"CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems","venue":"cs.LG","work_id":"66ac23ae-36d4-44ca-8c67-e928bbde7217","year":2023},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.939538Z"},"links":{"cited_paper":"/paper/2312.10527","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:a58392f4bdc57299c5963f1a03ed02c508195947946437a89644dbdf0930bba7","observation_id":"f0277a39-24b0-47fc-bdef-9ca00cecdb43","resolution":{"observed_at":"2026-08-05T19:56:11.122377Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.274408Z","title":"IEEE Access 7, 143608–143619 (2019)","venue":null,"work_id":"8956d72f-bad7-4015-8e77-678fd17f0e73","year":2019},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.942352Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:6e08ddc8afde98dbbd1505ee46be461fd8f80458ed2fd0352c6baf28fd596388","observation_id":"b816162c-9b29-492d-bd46-0c10ec76466c","resolution":{"observed_at":"2026-08-05T19:56:11.277151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.265614Z","title":"Advances in neural information processing systems34, 21696–21707 (2021)","venue":null,"work_id":"9938a6e9-8d42-4eaf-9eef-b43dc75bf248","year":2021},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.945051Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:bd632fcf3cac42b3ec2aafd2d6e0eaa48825eb11d74bd817935a921b1f2f2892","observation_id":"3f77451a-169a-43e4-a9e5-a6d6346e8075","resolution":{"observed_at":"2026-08-05T19:56:11.268495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.256951Z","title":null,"venue":null,"work_id":"f6991ccc-e48b-4439-9e35-2d4cafe63642","year":2022},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.947663Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:f872c54c57f0ebfb2316f10fe2cc88ec038cd47da0ce7a25488a984cfcbba7e1","observation_id":"fa7c31a3-e840-4dc9-9dfe-081086dbf887","resolution":{"observed_at":"2026-08-05T19:56:11.259732Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.248441Z","title":null,"venue":null,"work_id":"74846bdd-1c75-4c0e-b61c-97db532ddecb","year":2017},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.950109Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:7379932c5403a4c61cfef77f0fbe6281e5ac8c32d720a851a1cd32e6718a6303","observation_id":"d95d35a8-68f3-4391-afda-0c01ea9be2fc","resolution":{"observed_at":"2026-08-05T19:56:11.251345Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.239620Z","title":"In: Interna- tional conference on artificial neural networks","venue":null,"work_id":"6d2a02b1-d602-4519-b4e2-69cc67d73aae","year":2019},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.952708Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:422c11630d31774278589db52df3e3f7cdabf461a1a1f7cd8b9747cbd93481ff","observation_id":"b8be3e81-2e3d-436c-af1a-d6f577f1b42e","resolution":{"observed_at":"2026-08-05T19:56:11.242251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.230755Z","title":"In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","venue":null,"work_id":"8c5c1c45-e958-4d49-9994-7055a78999d6","year":2020},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.955448Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:f1849810cad24c8590f999488b086de93268ff1205cd9cda7f7afef69479ff4d","observation_id":"04b1d82f-2c0e-4ac1-b2d7-64489af3f7bc","resolution":{"observed_at":"2026-08-05T19:56:11.234000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.221070Z","title":"Machine Learning for Computational Science and Engineering1(1), 1–23 (2025)","venue":null,"work_id":"3385e332-b6ef-4e53-a0ee-a8ecf6276dfb","year":2025},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.958259Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:9d63e06746150187ad0c5168682b1e17bc0153711153318256eed23d286ba4aa","observation_id":"e84cc815-70a2-4466-8707-fea858e72365","resolution":{"observed_at":"2026-08-05T19:56:11.224175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.211673Z","title":"In: 2020 IEEE Power & Energy Society General Meeting (PESGM)","venue":null,"work_id":"64c99c3f-eeca-4b96-aa98-1d16427f289a","year":2020},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.960938Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:741d47ddfc6441c5d4ca9c8d26b2f422254046a32127dfdd56ce16ccb1a07dec","observation_id":"adfbf9e5-ec36-42b3-b3b1-35bb3243d27d","resolution":{"observed_at":"2026-08-05T19:56:11.215215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T19:56:10.963737Z","title":"Engineering Applications of Arti- ficial Intelligence 131, 107696 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.963737Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:a341556cadcaca7d91972211c7de25c14f1af195c1196db417f974d595eb66b2","observation_id":"ae29a115-6253-46e5-93e0-00158ae18b71","resolution":{"observed_at":"2026-08-05T19:56:10.963737Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.202467Z","title":"MIT Press (2022), probml.ai","venue":null,"work_id":"358f4770-1c68-4608-b026-183917b0d4f6","year":2022},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.966443Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:c524914f78fb054ef914d55f4b5708b1661577a06d3a75a2072f5893806409ef","observation_id":"d3d3558f-78a5-4335-b13e-68e0a38d608e","resolution":{"observed_at":"2026-08-05T19:56:11.205368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.193114Z","title":"MIT Press (2023), http://probml.github.io/book2","venue":null,"work_id":"59e3bc87-80ba-442f-9b6c-11a6356af0c1","year":2023},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.969028Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:8bcb6fde4060f629adf29bb9428747a397bf2f007b42738cfc7dca138c3b0a81","observation_id":"5d8ac42c-4f01-4144-a802-fcde64cd2327","resolution":{"observed_at":"2026-08-05T19:56:11.196451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.184609Z","title":"In: International Conference on Machine Learning","venue":null,"work_id":"5eb58bd0-f61f-4f83-a4c5-61e2576569ee","year":2021},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.971554Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:9c943eda5d3957c98aca3fed2981b1dcc4cb39e19d48bfa96daf135d4d06d144","observation_id":"5a025a96-e2c1-4dec-9370-c5b8a3fc5473","resolution":{"observed_at":"2026-08-05T19:56:11.187440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.175639Z","title":"Sensors 20(13), 3738 (2020)","venue":null,"work_id":"d1d8ce0a-6328-4c17-93a8-835c793a687b","year":2020},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.974098Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:c3a028fe9b7da82a3bc2c0f65dc3e7142b8897283f3945d32946e170a9a0e30a","observation_id":"91f572e3-7904-41ee-bf70-b5e73eb2307e","resolution":{"observed_at":"2026-08-05T19:56:11.178634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:56:11.165968Z","title":"In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW)","venue":null,"work_id":"3aeb954a-9959-4535-870f-1669ab191291","year":2023},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.976730Z"},"links":{"citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:8a922a356fcccdc0c346c3bf1c3d13dabe9a796686aeb64eb2db5a293e00a227","observation_id":"d74fe7c6-410a-4fe2-87e7-9538b86aa7c3","resolution":{"observed_at":"2026-08-05T19:56:11.169511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10561","last_updated":"2017-11-28T21:21:59Z","snapshot_observed_at":"2026-07-06T06:11:42.746828Z","submitted_at":"2017-11-28T21:21:59Z","title":"Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10561","snapshot_observed_at":"2026-08-05T19:56:10.979390Z","title":"arXiv preprint arXiv:1711.10561 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.979390Z"},"links":{"cited_paper":"/paper/1711.10561","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:006d66ddea4762b3560d35e253edf32e3ba0f140a52edf41f2122ca5e3a0b59a","observation_id":"edcd5777-16e7-4f3d-bb93-5ce5637389c6","resolution":{"observed_at":"2026-08-05T19:56:10.979390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10566","last_updated":"2017-11-28T21:29:35Z","snapshot_observed_at":"2026-07-06T06:11:42.746828Z","submitted_at":"2017-11-28T21:29:35Z","title":"Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10566","snapshot_observed_at":"2026-08-05T19:56:10.982333Z","title":"arXiv preprint arXiv:1711.10566 (2017) 16 J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.982333Z"},"links":{"cited_paper":"/paper/1711.10566","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:513ed36e4eb2f67302c3079d0e4eb7658b9028bfbe617c021996804aca7f863b","observation_id":"e5e1bfe0-2fe2-4519-a927-61b21664b169","resolution":{"observed_at":"2026-08-05T19:56:10.982333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T01:00:08.496074Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":31},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2508.11528."}