{"as_of":"2026-08-09T14:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:987413d2091b4753f619a3897af59fdcc7448c2242722583ae11a2a41bc9c018","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T08:25:36.012333Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.29370/citation-record","integrity":"/paper/2607.29370/integrity","json":"/paper/2607.29370/citation-record.json","paper":"/paper/2607.29370"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T08:25:29.977416Z","title":"Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:29.977416Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:382f67fb73f1b7dd7b6b8c54eba4d7377bd69d64021c9e0193731907e20033a4","observation_id":"3442c5a1-59bb-41e8-b6b9-e5538e57cd7d","resolution":{"observed_at":"2026-08-03T08:25:29.977416Z","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":"2026-08-03T08:25:30.110454Z","title":"Classification Problem Solving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:30.110454Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:b0eb2ca558da5658bba151bc97f8c0722ef07bbea0f5e188044d7e9b6da430c2","observation_id":"3a193bd3-8c3e-4f38-af13-114c6f62db31","resolution":{"observed_at":"2026-08-03T08:25:30.110454Z","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":"2026-08-03T08:25:30.292554Z","title":", title =","venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:30.292554Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:54667ff00ab4f2ff58e4c3f09aa0a08c6fcff1aadfdcfb62fd2f96f5fb775f9b","observation_id":"7431171c-64d3-4f1f-b3ff-3582d6ba124d","resolution":{"observed_at":"2026-08-03T08:25:30.292554Z","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":"2026-08-03T08:25:30.434692Z","title":"New Ways to Make Microcircuits Smaller---Duplicate Entry","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:30.434692Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:d70c36d66d25b391fe72c2b1bd409f3a376c0ec3ed48e451f6798f76526a4f76","observation_id":"590675da-ea69-4821-8eca-211cc4c8a132","resolution":{"observed_at":"2026-08-03T08:25:30.434692Z","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":"2026-08-03T08:25:30.586139Z","title":"Clancey and Glenn Rennels , abstract =","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:30.586139Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:00c0ddd028358c023b6558b8a6c4019a72397f9856c7243058405377dbd91365","observation_id":"9f6c02ed-a642-4d7a-9572-818f56b0b1f4","resolution":{"observed_at":"2026-08-03T08:25:30.586139Z","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":"2026-08-03T08:25:30.728430Z","title":"and Rennels, Glenn R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:30.728430Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:11721f2585bc9efcfc24130f920f64e5ed73c64864b1a6f0ffb8d17a00ca667d","observation_id":"05f5b790-0def-4cf5-b005-91871437736f","resolution":{"observed_at":"2026-08-03T08:25:30.728430Z","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":"2026-08-03T08:25:30.922377Z","title":"Poligon: A System for Parallel Problem Solving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:30.922377Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:df21ae196805292ee460225115154939744d73f3b925c792d67739f8d6b2b367","observation_id":"6721f500-b05c-4ed2-9546-aa60ce1caadf","resolution":{"observed_at":"2026-08-03T08:25:30.922377Z","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":"2026-08-03T08:25:31.030947Z","title":"Transfer of Rule-Based Expertise through a Tutorial Dialogue","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.030947Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:3a9544ac5bda96796e33e069d53d3a7b11f2d0f98e42d804d94823a545402b01","observation_id":"cfaab6cb-e9a0-4e9d-b30e-67940fa9bbcc","resolution":{"observed_at":"2026-08-03T08:25:31.030947Z","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":"2026-08-03T08:25:31.150374Z","title":"The Engineering of Qualitative Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.150374Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:69be0945f5f5163b0b84d9c67d06626bb2a11131250beb3fd9ee6390c5428f2d","observation_id":"234890e8-be21-4247-8b8c-de333bca83b4","resolution":{"observed_at":"2026-08-03T08:25:31.150374Z","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":"2026-08-03T08:25:31.260716Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.260716Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:a70f3cc02ac91c3934236a87043b20ee126bcebb83ef60b5f153ea620408f72b","observation_id":"a3f77ebd-fd28-407d-bd5c-8bdb12c54091","resolution":{"observed_at":"2026-08-03T08:25:31.260716Z","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":"2026-08-03T08:25:31.357841Z","title":"Pluto: The 'Other' Red Planet","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.357841Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:d38dc28658c2160f8c42e37a117e98a53e21cf42af0cc2275a071b1df99b636e","observation_id":"2f843337-33cc-49ff-bd0d-9d8d42469160","resolution":{"observed_at":"2026-08-03T08:25:31.357841Z","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":"2026-08-03T08:25:31.461661Z","title":"Pattern Recognition , pages=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.461661Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:18cc423137b5be3da3f8823e23a71bcf7f95c356c597f539c37aae563749354b","observation_id":"54ae4de5-aacb-444f-81a1-2e6bee3c5cd1","resolution":{"observed_at":"2026-08-03T08:25:31.461661Z","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":"2026-08-03T08:25:31.537369Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.537369Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:03422490293ed590100ae914fd2e6fbcc4c94c94c1880eb4b44cf83166901193","observation_id":"3dcc7a1b-6d38-4d08-b174-4d2838a68ee6","resolution":{"observed_at":"2026-08-03T08:25:31.537369Z","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":"2026-08-03T08:25:31.631013Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.631013Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:126d03c2e01114bbd40fa641b1cc3681437039d2250492f09f58946a52970205","observation_id":"6f050212-b65b-40f4-9283-919a1ea9fe67","resolution":{"observed_at":"2026-08-03T08:25:31.631013Z","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":"2026-08-03T08:25:31.765250Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.765250Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:1b33e7de4c311f86e83cd992f65010e8937b2e3e88df5e297f3264908f1d1d7f","observation_id":"e1d6bda8-c0cd-4c6a-b872-ac33d97e22a3","resolution":{"observed_at":"2026-08-03T08:25:31.765250Z","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":"2026-08-03T08:25:31.873118Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.873118Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:dce1fdc952735505f9ac044b0700f70f07d0934bc1ad9ba89b4b0f95481ba11f","observation_id":"813f18f8-c9e1-4a2e-8776-f7740112b74a","resolution":{"observed_at":"2026-08-03T08:25:31.873118Z","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":"2026-08-03T08:25:31.950035Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:31.950035Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:68ec2dc0cf4f64c07c38ecc10c3ef618bf469c5a3de052a168ae3010323d16e7","observation_id":"d0153262-b432-4da1-80d5-8413940e7aab","resolution":{"observed_at":"2026-08-03T08:25:31.950035Z","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":"2026-08-03T08:25:32.057371Z","title":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) , pages=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.057371Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:1645e7a4466f651f3c0cd04da19b011e5bfd2a35237da0620ce96347b18f1dbd","observation_id":"811a0965-ac22-4e92-a5ff-f21b6d8dfc9c","resolution":{"observed_at":"2026-08-03T08:25:32.057371Z","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":"2026-08-03T08:25:32.152803Z","title":"DAGM symposium in , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.152803Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:bb2aa9de72d965aeb99e49ac2359971d6ffe89e28cc6bb35477f23df6579dce6","observation_id":"d945bee3-2e02-41a6-944c-0b8167888902","resolution":{"observed_at":"2026-08-03T08:25:32.152803Z","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":"2026-08-03T08:25:32.263954Z","title":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.263954Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:dac1b9a3d5c4170b858911b36b06ea4fcd4e6f7c030c07f414922fcbb5f22ee7","observation_id":"5c85badf-b77b-4863-9e5b-f64b6be0fd53","resolution":{"observed_at":"2026-08-03T08:25:32.263954Z","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":"2026-08-03T08:25:32.371186Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.371186Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:802ccc4e274d8d3315a41cce18b428bd2de419691cf07c3164e54662d5a7c7ec","observation_id":"e6a76d57-c67a-4711-8bf7-9fd075c316cf","resolution":{"observed_at":"2026-08-03T08:25:32.371186Z","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":"2026-08-03T08:25:32.444308Z","title":"Brain , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.444308Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:b59593c0482d2b258f2fb92979a79d819e6b86a3ed700c6f90888d91fc6200a1","observation_id":"90084e85-eba0-4238-8b12-addd8276cb98","resolution":{"observed_at":"2026-08-03T08:25:32.444308Z","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":"2026-08-03T08:25:32.503309Z","title":"2020 , howpublished =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.503309Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:c548423be195eaf529ff06123d95aafb7fdc21ed2055c9a4713ffc69c7f18fe0","observation_id":"a0fe916b-4c6b-44b3-abd1-52d3bee386f7","resolution":{"observed_at":"2026-08-03T08:25:32.503309Z","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":"2026-08-03T08:25:32.612360Z","title":"IEEE transactions on medical imaging , volume=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.612360Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:c6cd3d36780bc72145efedd115e82c713fb456d3e74ae62926d480dba73da592","observation_id":"2fa516db-d655-4aee-a6bb-426e644b6cf7","resolution":{"observed_at":"2026-08-03T08:25:32.612360Z","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":"2026-08-03T08:25:32.717938Z","title":"saliency maps from physicians , author=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.717938Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:10c8ec6fb0e4458ff050d3b986996f91ddbc423418bf18ee749dccd0d65a19b9","observation_id":"16986e20-a728-47f5-9882-8aa60ee232e4","resolution":{"observed_at":"2026-08-03T08:25:32.717938Z","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":"2026-08-03T08:25:32.825227Z","title":"Pattern Recognition","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.825227Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:12926703b5db550d8db412a51e735b1e59558b147462517b50fcde82760a57af","observation_id":"e30d1601-00f7-429a-8397-fffb1d2d6ab2","resolution":{"observed_at":"2026-08-03T08:25:32.825227Z","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":"2026-08-03T08:25:32.951120Z","title":"International conference on multimedia modeling , pages=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:32.951120Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:47c247bfaf9572ae10ebed00dce75232d2f75bd1ec02ae110df5b5fa9e73c8b1","observation_id":"37c7b6ec-a309-4ee0-b915-8bf35a067702","resolution":{"observed_at":"2026-08-03T08:25:32.951120Z","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":"2026-08-03T08:25:33.043078Z","title":"2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) , pages=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.043078Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:48e40162188d390aeb581e7e3ef70dcab32f3350081493bb126b5e917ab06090","observation_id":"809ea8bb-0883-4e70-a788-1e0ee46c683d","resolution":{"observed_at":"2026-08-03T08:25:33.043078Z","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":"2026-08-03T08:25:33.206316Z","title":"Computers in Industry , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.206316Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:91149237bba87d72f5b9142f96e1363cfcae7ed47130be077ac2b507c8c189c8","observation_id":"bbd705e6-4766-4031-87a0-0ba03c0d6aeb","resolution":{"observed_at":"2026-08-03T08:25:33.206316Z","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":"2026-08-03T08:25:33.323318Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.323318Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:1436d12e16f09902a7e58210d7bf1ea6c6791a4e4f33ec3d0c315ae2b5cc7a80","observation_id":"8c4aa201-8826-4f09-8f45-f25b2ea7f01b","resolution":{"observed_at":"2026-08-03T08:25:33.323318Z","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":"2026-08-03T08:25:33.485341Z","title":"European conference on computer vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.485341Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:7bbd23b994a1af3913084d6edf5bb73fda89c239e0741eb5e155a9624205078c","observation_id":"4985bf58-9384-4507-98e1-86f01e5b0cd1","resolution":{"observed_at":"2026-08-03T08:25:33.485341Z","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":"2026-08-03T08:25:33.649916Z","title":"International Conference on Learning Representations , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.649916Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:fe5f35302654885a0f1b9726f02678723d89773ca82cd4426d225483bff527a7","observation_id":"a1f92c9f-8de9-44d6-9bcb-126f000116cc","resolution":{"observed_at":"2026-08-03T08:25:33.649916Z","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":"2026-08-03T08:25:33.777213Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.777213Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:21049c791fee254cbf0c17f3cc492cb5b856c5aef68d96b82d705ef1284ee817","observation_id":"4dd46199-d982-4503-94d0-f5aa2a5223e2","resolution":{"observed_at":"2026-08-03T08:25:33.777213Z","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":"2026-08-03T08:25:33.937031Z","title":"ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:33.937031Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:df2bb16458f603a3484f3b4800f9cf2324cb218392deb4c62d3de60e51564f5b","observation_id":"65fd1888-239a-4d45-86d5-2167ca15777e","resolution":{"observed_at":"2026-08-03T08:25:33.937031Z","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":"2026-08-03T08:25:34.061788Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.061788Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:9b73011b9bdea1942dd05a19f9aa0d47aa2960c6a35ed2c836e8efa34819b921","observation_id":"dc5dd6a7-cf2a-44d9-860e-ad59e648b040","resolution":{"observed_at":"2026-08-03T08:25:34.061788Z","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":"2026-08-03T08:25:34.195542Z","title":"International journal of computer vision , volume=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.195542Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:018ed7d26afd7856346654042f56bfd3abb5eb983f8bf40400bdb76399b90581","observation_id":"95865afd-83c3-4d07-acf3-04072d83a4ac","resolution":{"observed_at":"2026-08-03T08:25:34.195542Z","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":"2026-08-03T08:25:34.380687Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.380687Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:f1aac0e42e937678950eb4575f87036d723cc740eb8bb3905f19b49f8b248ddc","observation_id":"2b99d2af-7afd-456d-ad59-64333ae05461","resolution":{"observed_at":"2026-08-03T08:25:34.380687Z","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":"2026-08-03T08:25:34.506901Z","title":"Proceedings of the IEEE/CVF international conference on computer vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.506901Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:d2aebcb2ac0170c8c9f84964aa35ab4518cab5c92db9f7964b41944e785b6292","observation_id":"0f78e2e2-ccd1-43ac-b91b-8e4b702afc5f","resolution":{"observed_at":"2026-08-03T08:25:34.506901Z","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":"2026-08-03T08:25:34.638250Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.638250Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:335930495e9f32eb1b3315800cbef7e78f3cb0d7a4eeb1caf9f15e655f8fde87","observation_id":"c52d475e-0323-4f17-9c5a-68512fcc33a9","resolution":{"observed_at":"2026-08-03T08:25:34.638250Z","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":"2026-08-03T08:25:34.733028Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.733028Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:30c1d30120a8b7fcc20dcf7d6a31291736eb26a464909fbf7d632c3bce3ed0e9","observation_id":"dd261150-d5b5-4c66-8e66-a5bee4c441e7","resolution":{"observed_at":"2026-08-03T08:25:34.733028Z","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":"2026-08-03T08:25:34.882774Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:34.882774Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:398d6a5320d26f3f01b3456de8b8e03b7e03cc6104496807749e9b39273f9d8c","observation_id":"e65f9461-a905-4f76-9376-5859c3b35b19","resolution":{"observed_at":"2026-08-03T08:25:34.882774Z","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":"2026-08-03T08:25:35.001596Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.001596Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:6229a4a98feab4520fb358cb7c2d6cb06129546038258138c118ee5a68bdffec","observation_id":"2e1f10bc-248c-4d2b-ad56-b7e43524b3f8","resolution":{"observed_at":"2026-08-03T08:25:35.001596Z","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":"2026-08-03T08:25:35.170812Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.170812Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:3b54deade56ab79db2163023974e2770ff3a86bf4a54c507a81ead8250eb23c4","observation_id":"4d47d23a-acc1-422a-b361-845fa9c8f128","resolution":{"observed_at":"2026-08-03T08:25:35.170812Z","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":"2026-08-03T08:25:35.258730Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.258730Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:d0a3c5dcba961d2ccaa526ed73441b9353094d740ed003c75c7237123e62c066","observation_id":"7a7f40b4-20c3-4b8e-afb7-fc08ba851a86","resolution":{"observed_at":"2026-08-03T08:25:35.258730Z","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":"2026-08-03T08:25:35.380264Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.380264Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:aa943eda980c0bfece2754fecdcf1b58f9ee2b764791ee5c03d71595deb094f6","observation_id":"9a5c0bed-8a56-4cef-9240-8512a0c9115a","resolution":{"observed_at":"2026-08-03T08:25:35.380264Z","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":"2026-08-03T08:25:35.447989Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.447989Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:038f8815d3880309cda46832b99e2e71c84442d58bde1850e115047bc9d2193a","observation_id":"d42ff013-65a7-4af3-bcc1-b9d7e5d0c23d","resolution":{"observed_at":"2026-08-03T08:25:35.447989Z","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":"2026-08-03T08:25:35.518996Z","title":"The Thirteenth International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.518996Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:b92a5e1467b8eda26c4e2e821c4a9dfa6e86f3f642b0df4bdf93c28a6a512b14","observation_id":"1378ca3c-aabd-403d-982f-4c00af143430","resolution":{"observed_at":"2026-08-03T08:25:35.518996Z","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":"2026-08-03T08:25:35.601110Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.601110Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:4f097d4a9b83a90896e72f248a18d5cc0e1381ba940abaa92dcf23053976eb72","observation_id":"f8c551ea-a1de-4ba0-9ec2-fec00f604433","resolution":{"observed_at":"2026-08-03T08:25:35.601110Z","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":"2026-08-03T08:25:35.728283Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.728283Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:b2d3d73610283d413e9d2dff8a3af469e1c088cb3ed61b9dcac6001256addfab","observation_id":"ee7d1ead-5f4b-4c2f-946d-27dbe76e6882","resolution":{"observed_at":"2026-08-03T08:25:35.728283Z","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":"2026-08-03T08:25:35.859110Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.859110Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:4fee979f4382db5600707d2bbb6391374422661cc39030cd168ed759d7fb68a4","observation_id":"25a482cc-5840-4339-a6e4-842db7e1450f","resolution":{"observed_at":"2026-08-03T08:25:35.859110Z","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":"2026-08-03T08:25:35.968716Z","title":"Proceedings of the IEEE international conference on computer vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:35.968716Z"},"links":{"citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:348eac85b15c2007792a73673804c368163ed1eb0c695138b9a3baf82532141d","observation_id":"945c31eb-bc1e-4b04-8110-eb5e46030a3a","resolution":{"observed_at":"2026-08-03T08:25:35.968716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-01T23:09:00.410365Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-03T08:25:36.012333Z","title":"arXiv preprint arXiv:1911.02855 , year=","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:36.012333Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:3ead2739da63285328a61a29d958f7810295e0931dcd14d7b19cdee8e099db28","observation_id":"e35ed05b-238c-4e6b-84c8-628b93e02472","resolution":{"observed_at":"2026-08-03T08:25:36.012333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T06:57:32.927179Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":52},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2607.29370."}