{"as_of":"2026-08-05T03:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2c6f315f9a534543e39f2819813c39ae3d1f38b538f5fc57a57ff5226963fcd9","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T19:27:53.131940Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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/2509.09251/citation-record","integrity":"/paper/2509.09251/integrity","json":"/paper/2509.09251/citation-record.json","paper":"/paper/2509.09251"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:27:50.780680Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:50.780680Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:7faadf0c126e8622bb704ee36c17a933a1f23de3d13b0081514784b4521d57ee","observation_id":"f3570eab-a432-4416-bd7c-2052ed46ae2a","resolution":{"observed_at":"2026-08-04T19:27:50.780680Z","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":"2026-08-04T19:27:50.910910Z","title":"Isermann","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:50.910910Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:14525fd8e0bcf0ca7676ec202e3af15d9cc67d0c7487541f4ef8a5deff6849ee","observation_id":"cb9a6f55-f661-43c1-afc3-fc3feaba26ee","resolution":{"observed_at":"2026-08-04T19:27:50.910910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.00589","last_updated":"2022-07-03T08:52:15Z","snapshot_observed_at":"2026-07-06T13:26:56.095858Z","submitted_at":"2022-07-03T08:52:15Z","title":"SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.00589","snapshot_observed_at":"2026-08-04T19:27:51.010383Z","title":"Ssd-faster net: A hybrid network for industrial defect inspection.arXiv preprint arXiv:2207.00589, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.010383Z"},"links":{"cited_paper":"/paper/2207.00589","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:622f6c8d4177f749d7f223ae5c0aac5a28ecdd30cbf8a9a34731c586c73112e9","observation_id":"59f8551a-1881-444c-a859-1b904cf52a83","resolution":{"observed_at":"2026-08-04T19:27:51.010383Z","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":"2026-08-04T19:27:51.151345Z","title":"Moraru, M","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.151345Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:7721e9213b3c340df9d79bbfa96a5a7b5ba0a0186d7e4cef4c8af857e255c63e","observation_id":"00bf2243-b174-4205-b0e4-f6c9c95c9fe3","resolution":{"observed_at":"2026-08-04T19:27:51.151345Z","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":"2026-08-04T19:27:51.268202Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.268202Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:a17f645333ae362da12b9d15dc8c60a2c620b0604ce6a91d9f96533376ff4b48","observation_id":"3b019675-a2e2-45da-8720-ec09b5edf5bf","resolution":{"observed_at":"2026-08-04T19:27:51.268202Z","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":"2026-08-04T19:27:51.335793Z","title":"Blasch, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.335793Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:b4adf9a6b372d5d6289fe7b195bb49a3c2fe6c2a6e67a4ba46bcaf932619d9fb","observation_id":"c17de84e-8089-4451-b566-1bc4fa75bd94","resolution":{"observed_at":"2026-08-04T19:27:51.335793Z","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":"2026-08-04T19:27:51.394947Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.394947Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:7f586ce39932c4577b2f4673adec7093fb505378aacc4afb209d52bf26b1da7c","observation_id":"b62e2415-9a34-4969-a7dd-a218ca9521f2","resolution":{"observed_at":"2026-08-04T19:27:51.394947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09676","last_updated":"2024-08-19T03:33:39Z","snapshot_observed_at":"2026-07-06T19:02:15.690517Z","submitted_at":"2024-08-19T03:33:39Z","title":"Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09676","snapshot_observed_at":"2026-08-04T19:27:51.443781Z","title":"Image- based freeform handwriting authentication with energy-oriented self- supervised learning.arXiv preprint arXiv:2408.09676, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.443781Z"},"links":{"cited_paper":"/paper/2408.09676","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:29f69efb7b6160fde8ae5753741c0699f15ab2f00b246e05572d585f8a77313d","observation_id":"b531dc57-bd15-49b1-aec2-d7dafaf25fb8","resolution":{"observed_at":"2026-08-04T19:27:51.443781Z","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":"2026-08-04T19:27:51.489130Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.489130Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:05266b6c8fddb7767196b479bde86ae43a05988792c4e327c7e341af9599acd1","observation_id":"824a90f7-0601-4048-abb5-ca34b09e892d","resolution":{"observed_at":"2026-08-04T19:27:51.489130Z","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":"2026-08-04T19:27:51.546493Z","title":"Camps-Valls","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.546493Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:d68bdf2bf24746825b63c0633f57faae2708de96afb3d35eece6c9e40915e2f2","observation_id":"e1696e5e-1e6d-4640-ba3e-bccb76989d3b","resolution":{"observed_at":"2026-08-04T19:27:51.546493Z","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":"2026-08-04T19:27:51.592529Z","title":"Gupta, M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.592529Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:001705eaf816ccc1536bff73b369aaf229a600e4ed12467847fc80db5f61b4a5","observation_id":"9c76484c-ee4f-43b8-8ea0-97231860c81e","resolution":{"observed_at":"2026-08-04T19:27:51.592529Z","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":"2026-08-04T19:27:51.667250Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.667250Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:8fa544f1a1d2b17411032a4d61db7270f86c5119a2d8b89308dd6a8957953070","observation_id":"2ede0c72-caa2-4495-af88-8efe5d227f69","resolution":{"observed_at":"2026-08-04T19:27:51.667250Z","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":"2026-08-04T19:27:51.710571Z","title":"Advanc- ing complex wide-area scene understanding with hierarchical coresets selection.arXiv preprint arXiv:2507.13061, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.710571Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:dcee7c091351d4520659b1244b032bad724ca3e9fbeaac4b6055edb99eb5e3d4","observation_id":"517d5a22-4cea-4d20-9c8b-520015b07aaf","resolution":{"observed_at":"2026-08-04T19:27:51.710571Z","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":"2026-08-04T19:27:51.780320Z","title":"Model-agnostic meta- learning for fast adaptation of deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.780320Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:45094f8eb0b6dda8c5b12dc140f47b32937dcb498554fe57ebfee595d377a666","observation_id":"f68b55eb-5794-4bb2-8ebf-77a868c8a9fd","resolution":{"observed_at":"2026-08-04T19:27:51.780320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01053","last_updated":"2025-05-16T12:49:48Z","snapshot_observed_at":"2026-07-06T18:08:35.173140Z","submitted_at":"2024-05-02T07:15:23Z","title":"On the Universality of Self-Supervised Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01053","snapshot_observed_at":"2026-08-04T19:27:51.863790Z","title":"Explicitly modeling generality into self-supervised learning.arXiv preprint arXiv:2405.01053, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.863790Z"},"links":{"cited_paper":"/paper/2405.01053","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:3731fb06dec590ca23b9a13104eb8c898904d5c4a015aa8e67c1b2e643c9d85a","observation_id":"718a71f3-72d5-46ac-898a-3c2061f6c667","resolution":{"observed_at":"2026-08-04T19:27:51.863790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.09157","last_updated":"2020-02-12T15:29:39Z","snapshot_observed_at":"2026-07-06T08:23:10.708764Z","submitted_at":"2019-09-19T16:30:42Z","title":"Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.09157","snapshot_observed_at":"2026-08-04T19:27:51.925502Z","title":"Rapid learningorfeature reuse? towards understandingtheeffectiveness of maml.arXiv preprint arXiv:1909.09157, 2019","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:51.925502Z"},"links":{"cited_paper":"/paper/1909.09157","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:ebf048df25f9bc65e91f35275b8d33c2b3d76a8c72b54cf20f7e888db4c025fd","observation_id":"0a3007e7-38d7-4814-8120-ea1c2fa698ef","resolution":{"observed_at":"2026-08-04T19:27:51.925502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.12024","last_updated":"2024-04-18T09:21:16Z","snapshot_observed_at":"2026-08-04T13:52:57.085162Z","submitted_at":"2024-04-18T09:21:16Z","title":"Meta-Auxiliary Learning for Micro-Expression Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.12024","snapshot_observed_at":"2026-08-04T19:27:52.000401Z","title":"Meta-auxiliarylearningformicro-expression recognition.arXiv preprint arXiv:2404.12024, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.000401Z"},"links":{"cited_paper":"/paper/2404.12024","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:013fd61dfbbb49a3093765226f9d9b49be32a362b7528fac3a706e951f1c145f","observation_id":"74587ee0-6391-49d5-96df-24963d97c453","resolution":{"observed_at":"2026-08-04T19:27:52.000401Z","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":"2026-08-04T19:27:52.071456Z","title":"Towards task sampler learning for meta-learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.071456Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:cd1613a8082420a15aff7fdfa752627368b08836670e9ec8a3e80368dabe7e67","observation_id":"2a1b3568-d3cf-4040-b4d2-fb218c5a5a8d","resolution":{"observed_at":"2026-08-04T19:27:52.071456Z","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":"2026-08-04T19:27:52.144902Z","title":"Prototypicalnetworksfor few-shot learning.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.144902Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:a9f592fa099ea65f97a75206534e831e496e9875858702a808cd58e93dedcc35","observation_id":"8e921c97-a634-4d50-ab05-d67fea154a8a","resolution":{"observed_at":"2026-08-04T19:27:52.144902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.02999","last_updated":"2018-10-22T16:11:14Z","snapshot_observed_at":"2026-07-06T06:27:14.996782Z","submitted_at":"2018-03-08T08:29:38Z","title":"On First-Order Meta-Learning Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.02999","snapshot_observed_at":"2026-08-04T19:27:52.207540Z","title":"Reptile: a scalable metalearning algo- rithm.arXiv preprint arXiv:1803.02999, 2(3):4, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.207540Z"},"links":{"cited_paper":"/paper/1803.02999","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:d571003f6252dce703287e8bbb03111e513da78b5d1da757726e80fbfec6b6d4","observation_id":"36fbbe15-9d7b-492b-bd1b-0e674ab24d2d","resolution":{"observed_at":"2026-08-04T19:27:52.207540Z","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":"2026-08-04T19:27:52.261160Z","title":"Vibration-based condition monitoring: Industrial, aerospace and automotive applications","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.261160Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:7668299004d14c368124b893214893a8a627f41623dd37841cd6be02d0a700c4","observation_id":"1b63c472-d246-452e-922a-1c9a6dd3ab78","resolution":{"observed_at":"2026-08-04T19:27:52.261160Z","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":"2026-08-04T19:27:52.343326Z","title":"Academic press, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.343326Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:ed6e9bbb95adcb54de5c3ca37d5d9ed64e7a8abc614b0b87dff3998e68a87bda","observation_id":"435d98b9-cdeb-48ae-b1da-6c2a4e9299d9","resolution":{"observed_at":"2026-08-04T19:27:52.343326Z","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":"2026-08-04T19:27:52.401875Z","title":"Bearing fault diagnosis based on multi-scale cnn and lstm network.Measurement, 103:5–12, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.401875Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:51178e689d1fda1d0afb4eaa457c8c4ed53833c695502591be3a4a54babec8fc","observation_id":"4139bc85-462f-4f64-a2a2-e3578f0eaa24","resolution":{"observed_at":"2026-08-04T19:27:52.401875Z","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":"2026-08-04T19:27:52.459800Z","title":"Noise-robust fault diagnosis method for rotating machin- ery based on time–frequency analysis and convolutional neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.459800Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:80df17dae0818a9aab82d1b135d01203b36a79d78b99d9a6a2b87810c92a7ef7","observation_id":"bae74de7-3372-4f95-8903-2d635ae3c59f","resolution":{"observed_at":"2026-08-04T19:27:52.459800Z","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":"2026-08-04T19:27:52.518779Z","title":"Time-frequency analysis in mechanical fault diagnosis–a review with applications.Mechanical Systems and Signal Processing, 121:209–237, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.518779Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:08d5de56dff44673763d514e69487eec2440636843df632778c4657377c9b336","observation_id":"b6f86c43-0442-46d0-80f0-1e49c63fe224","resolution":{"observed_at":"2026-08-04T19:27:52.518779Z","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":"2026-08-04T19:27:52.570964Z","title":"Springer, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.570964Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:4aec4947078f957ca93e90ae25fc3f1cf55081604043ac8ea06f9dc4e31d02cb","observation_id":"6ef370c9-4452-41e8-b754-8196a44b67f3","resolution":{"observed_at":"2026-08-04T19:27:52.570964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12921","last_updated":"2025-04-15T03:14:54Z","snapshot_observed_at":"2026-07-06T15:19:50.076606Z","submitted_at":"2023-04-24T03:09:25Z","title":"AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12921","snapshot_observed_at":"2026-08-04T19:27:52.631815Z","title":"Awesome- meta+: Meta-learning research and learning platform.arXiv preprint arXiv:2304.12921, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.631815Z"},"links":{"cited_paper":"/paper/2304.12921","citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:0bd5397645f714d7946e068598eeeb8c4d71cedfe3f01d5dc3d3fd49d5d89eae","observation_id":"60795c95-89fd-43a8-84cf-6b69238b0e24","resolution":{"observed_at":"2026-08-04T19:27:52.631815Z","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":"2026-08-04T19:27:52.689887Z","title":"Tfpred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis.Control Engineering Practice, 146:105900, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.689887Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:68bcdd4fdf05ec7c04cebb182278c27cd303ad277381bc1f864fb86a2c61e009","observation_id":"7317e77c-a686-482e-a3d4-ad21b91602c2","resolution":{"observed_at":"2026-08-04T19:27:52.689887Z","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":"2026-08-04T19:27:52.773088Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.773088Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:3b7dcb7e22ce18f5d6490e8c2b24c8f5db361e230cacf604175ef74ff177b14c","observation_id":"5477daac-db2f-49ee-81f8-d084e6e90d77","resolution":{"observed_at":"2026-08-04T19:27:52.773088Z","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":"2026-08-04T19:27:52.832909Z","title":"Boot- strap your own latent-a new approach to self-supervised learning.Ad- vances in neural information processing systems, 33:21271–21284, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.832909Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:34f1547b59eb73692746b13ddeae348656f8cc0f0c6aa99318557a5d8ed65cf1","observation_id":"08953202-cf62-4c9b-bbc2-dab11f053b12","resolution":{"observed_at":"2026-08-04T19:27:52.832909Z","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":"2026-08-04T19:27:52.918081Z","title":"Barlow twins: Self-supervised learning via redundancy reduction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.918081Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:e5481cdc992d719df501693f0af9366643d4e717a65e97e2a1cd603950e38890","observation_id":"00279a97-2356-4709-9cfc-8adbb53c7e8f","resolution":{"observed_at":"2026-08-04T19:27:52.918081Z","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":"2026-08-04T19:27:52.978111Z","title":"Learning to compare: Relation network for few-shot learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:52.978111Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:37a4625827059d04bdd5c10ce4a450a87694e08509ad394163da62625eb10db2","observation_id":"4b9d34ef-f0de-42ca-b2c2-c2ea36f958e9","resolution":{"observed_at":"2026-08-04T19:27:52.978111Z","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":"2026-08-04T19:27:53.022841Z","title":"FEDformer: Frequency enhanced decomposed transformer for long- term series forecasting.ICML, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:53.022841Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:b33125cbf795424de3ee0dd63edfa51860914c7908ad428c64b534dd18707168","observation_id":"bf9daec6-89ad-48d2-80a0-e520785aa1fd","resolution":{"observed_at":"2026-08-04T19:27:53.022841Z","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":"2026-08-04T19:27:53.080838Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis.ICLR, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:53.080838Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:9e69d5e89aeff2786eb4ac9cae88700d4f9047514136e012ed9699884d0b68a9","observation_id":"2411a6fa-0e3c-4285-a394-a2f8ca99a791","resolution":{"observed_at":"2026-08-04T19:27:53.080838Z","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":"2026-08-04T19:27:53.131940Z","title":"Bearingfaultdiagnosisbase onmulti-scalecnnandlstmmodel.Journal of Intelligent Manufacturing, 32(4):971–987, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T19:27:53.131940Z"},"links":{"citing_paper":"/paper/2509.09251"},"observation_digest":"sha256:05517732c7a99e07ff1d568f805063920fdef79cc19f0524d95f08c6644397bb","observation_id":"8cbb8160-37c6-4437-8616-606d7d89e4b0","resolution":{"observed_at":"2026-08-04T19:27:53.131940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.09251","last_updated":"2025-09-11T08:35:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T19:27:50.111361Z","submitted_at":"2025-09-11T08:35:43Z","title":"Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":35},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2509.09251."}