{"as_of":"2026-08-10T19:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fd089a35c7c47655d57b5e8ec5ed80d713e23a7fc0ce8eebf439f51256a8ad89","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T05:24:00.305591Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2607.22786/citation-record","integrity":"/paper/2607.22786/integrity","json":"/paper/2607.22786/citation-record.json","paper":"/paper/2607.22786"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T05:23:57.080253Z","title":"High frequency data filtering","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.080253Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:3b9221c4c48dd7a5c3199265a7a6f32c79611acee545dc0985e7b2064fe27863","observation_id":"b319aeab-b147-4677-994c-6322d4ce09fc","resolution":{"observed_at":"2026-08-01T05:23:57.080253Z","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":"2026-08-01T05:23:57.219069Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.219069Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:1f2b679adf1011f4e4d9b040d6938d647ebcccf4778ac7e235d30884b8c652bb","observation_id":"ae557500-5308-4fb7-94aa-bbcc9c1895cc","resolution":{"observed_at":"2026-08-01T05:23:57.219069Z","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":"2026-08-01T05:23:57.382775Z","title":"Anomaly detection on big data in financial markets","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.382775Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:13bfa88c8c33ecf8d363dcad548e1be4d3ca328b53184b649d574067fb75d91e","observation_id":"ed9e9bc1-3796-47b9-8a1d-e87c502408d6","resolution":{"observed_at":"2026-08-01T05:23:57.382775Z","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":"2026-08-01T05:23:57.571101Z","title":"Overview of the transformer-based models for nlp tasks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.571101Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:522fe89edd8ec3b5590255ac417f0f88b6bc8fc58492bb06ea8e119a887e0980","observation_id":"dddd694a-d92e-4d46-9cce-fedf75178b89","resolution":{"observed_at":"2026-08-01T05:23:57.571101Z","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":"2026-08-01T05:23:57.651127Z","title":"Transformers in time series: A survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.651127Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:90b9c828666ecfbde8398b5581929dc853c3d147cceed1c840271bfd401638f4","observation_id":"8bab543a-2277-47ee-91f6-50b68b0b70f5","resolution":{"observed_at":"2026-08-01T05:23:57.651127Z","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":"2026-08-01T05:23:57.794197Z","title":"Custom framework for run-time trading strategies","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.794197Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:1eac9208aa393913b38b2db2261ce25cbc92d32f9d968d402960f81764077c81","observation_id":"513dd148-557c-450d-a9d1-decf95d1c69c","resolution":{"observed_at":"2026-08-01T05:23:57.794197Z","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":"2026-08-01T05:23:57.920361Z","title":"Evaluation metrics for un- supervised learning algorithms, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:57.920361Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:7c64792a7b8b66fb5c800a444dc871a51a9243de763d4656d401ca18a13473ff","observation_id":"a9900624-7002-474c-bc63-db53348a71c5","resolution":{"observed_at":"2026-08-01T05:23:57.920361Z","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":"2026-08-01T05:23:58.040767Z","title":"Generating artificial outliers in the absence of genuine ones — a survey.ACM Transactions on Knowledge Discovery from Data (TKDD), 15:1 – 37, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.040767Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:bfafff98a18ab6fff70e505f827b3ecfaadb360d50157f1b6a7d49f1cc0f5616","observation_id":"0b6051c3-5b16-4e5d-b5cc-e78694735227","resolution":{"observed_at":"2026-08-01T05:23:58.040767Z","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":"2026-08-01T05:23:58.157659Z","title":"Anomaly detection in time series: A comprehensive evaluation.Proc","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.157659Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:a9b874ada4c7e2a7274b6b107935c1867db02bc780dbed37bfb38e0f1815b073","observation_id":"075ab099-7ed2-47bc-83eb-8d2c9bce6811","resolution":{"observed_at":"2026-08-01T05:23:58.157659Z","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":"2026-08-01T05:23:58.309467Z","title":"Neural machine translation by jointly learning to align and translate, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.309467Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:6a916bf1c6d4146643c00c0ac99cbd6c5205b1950d6bf198b2b1b2271bd5bfdd","observation_id":"820a33a4-fabb-4dbb-bd6f-0286b4cc519d","resolution":{"observed_at":"2026-08-01T05:23:58.309467Z","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":"2026-08-01T05:23:58.463655Z","title":"Gomez, Lukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.463655Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:5e4bf88b4b4525abf11ebcc4ffaf60f901be1bea749e220460e3c4dc28cfd38b","observation_id":"159ae29c-3de3-4e61-a7ed-9dd4a9f5c23c","resolution":{"observed_at":"2026-08-01T05:23:58.463655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-01T05:23:58.518269Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.518269Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:a4021f4649c44799f3b0ce2a8a6aa569b17b3f7dad0fc57f6b6842c6b71559b9","observation_id":"f8db2508-af1d-40ff-9646-fb77d25debd3","resolution":{"observed_at":"2026-08-01T05:23:58.518269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.04468","last_updated":"2023-05-08T05:42:24Z","snapshot_observed_at":"2026-08-09T02:46:59.159622Z","submitted_at":"2023-05-08T05:42:24Z","title":"AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.04468","snapshot_observed_at":"2026-08-01T05:23:58.593868Z","title":"Anomalybert: Self-supervised transformer for time series anomaly detection using data degradation scheme.ArXiv, abs/2305.04468, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.593868Z"},"links":{"cited_paper":"/paper/2305.04468","citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:caa69a8689adedccb0f2c85cda37c789fd8708656742d8bde731c65302af39c8","observation_id":"9a7f06a9-6af9-42a1-b8e6-e4c5d0aba2c7","resolution":{"observed_at":"2026-08-01T05:23:58.593868Z","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":"2026-08-01T05:23:58.660459Z","title":"Position informa- tion in transformers: An overview.Computational Linguistics, 48:733–763, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.660459Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:d0aa7ff936b302615e896d95f33372182575bcd76dcea14bb4932def4f880609","observation_id":"4fb30e84-1969-4eb2-b688-2251659d0825","resolution":{"observed_at":"2026-08-01T05:23:58.660459Z","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":"2026-08-01T05:23:58.759807Z","title":"The transformer family version 2.0.https://lilianweng","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.759807Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:535e9100f26c6a0d22b1977d2d7a60af9971c042b76d64fb8e3409c77cc56108","observation_id":"57eea855-713f-49d6-ac7d-f02d5dcf98cc","resolution":{"observed_at":"2026-08-01T05:23:58.759807Z","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":"2026-08-01T05:23:58.824212Z","title":"Deep residual learning for image recognition, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.824212Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:56fb67ab95c395f41a855842cc5b2b7f882199d602961ddceb1374a078711861","observation_id":"d49cbe1b-0018-4b7c-954e-328307e8a8a9","resolution":{"observed_at":"2026-08-01T05:23:58.824212Z","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":"2026-08-01T05:23:58.879228Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.879228Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:72056bf1a90052b072c7ef4d2adf7f25a4ace9cbee2396e9a89696b347c28f05","observation_id":"08cd63d9-def9-4cc6-a949-766feb6e07a7","resolution":{"observed_at":"2026-08-01T05:23:58.879228Z","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":"2026-08-01T05:23:58.884938Z","title":"Transformers are rnns: Fast autoregressive transformers with lin- ear attention, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.884938Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:15bec80018a5dc333646920222d5311d6ac2019192e38b1e14bcdbd85678abf8","observation_id":"11ea2c33-90e7-4c93-ad93-e40534014daa","resolution":{"observed_at":"2026-08-01T05:23:58.884938Z","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":"2026-08-01T05:23:58.972699Z","title":"TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data.Proceedings of VLDB, 15(6):1201–1214, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:58.972699Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:8dd5b19f275632f93b40befb695b0a90cc7032a9545e0f93ee1ab6e7005d799e","observation_id":"ba06d926-4cae-4a63-b0dc-42c7a790eaeb","resolution":{"observed_at":"2026-08-01T05:23:58.972699Z","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":"2026-08-01T05:23:59.060340Z","title":"Are transformers effective for time series forecasting?, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.060340Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:886e93f37052088d823cdec153622a2c8070583e979b321a7d3e7f30ced9aef9","observation_id":"5bee7b39-7456-40ab-a5a8-8435b57165e5","resolution":{"observed_at":"2026-08-01T05:23:59.060340Z","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":"2026-08-01T05:23:59.101474Z","title":"Time series forecasting with transformer models and application to asset management.SSRN Electronic Journal, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.101474Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:75617979c7f8ed1e8db2a9e1bb5d334a5b4dbee998a87d43026a0ad602d35e7d","observation_id":"cb32af5b-3a54-4bec-8ada-c115142170cf","resolution":{"observed_at":"2026-08-01T05:23:59.101474Z","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":"2026-08-01T05:23:59.186020Z","title":"Vitis HLS Knowledge Base - Real-Time Systems - York Wiki Service.https://wiki.york.ac.uk/display/RTS/Vitis+HLS+ Knowledge+Base, July 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.186020Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:bed0a18e61710810a3bfbaa79e5e9201997058ccd29612671f75f6a562aa17d2","observation_id":"40224ac0-7e1d-4bf6-874d-4ca4d4c9e62a","resolution":{"observed_at":"2026-08-01T05:23:59.186020Z","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":"2026-08-01T05:23:59.235207Z","title":"Vitis High-Level Synthesis User Guide.https://docs.xilinx","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.235207Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:d03088666e7a534d21a30eb4a63d8351efe8627817b7db8b555ecd9e83ff35a3","observation_id":"ca0ff652-bf4c-4346-88f9-9837e0c08a54","resolution":{"observed_at":"2026-08-01T05:23:59.235207Z","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":"2026-08-01T05:23:59.308736Z","title":"C/RTL Co-Simulation in Vitis HLS","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.308736Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:71d5ff66c0ad9293dfb0a94bd34f092198d3336dde52e87849955dc110fa1879","observation_id":"b77a602a-0386-4e89-8ab0-5d8303d76b5f","resolution":{"observed_at":"2026-08-01T05:23:59.308736Z","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":"2026-08-01T05:23:59.375602Z","title":"Vitis hls: Pipelining loops.https://docs.xilinx.com/r/ en-US/ug1399-vitis-hls/Design-Principles, May 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.375602Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:8a1af6785de39778e2ac40792f9bb234ccd49ff33bef4a5f4d1ea1c311c45e05","observation_id":"845a4c4a-51d8-4a92-98ae-67cc6a791471","resolution":{"observed_at":"2026-08-01T05:23:59.375602Z","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":"2026-08-01T05:23:59.436951Z","title":"Intel high level synthesis: Best practices guide.https: //www.intel.com/content/www/us/en/docs/programmable/683152/ 21-3/pipeline-loops.html","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.436951Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:d868b27baa39a2033ad4b34e243718ca70f296c58b239758d90f5925c500bdd0","observation_id":"2022807a-485c-4cb0-bf85-02764b870608","resolution":{"observed_at":"2026-08-01T05:23:59.436951Z","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":"2026-08-01T05:23:59.591645Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.591645Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:41801a6e6af64b3b76f699a3ace5eb1eac2bb1a6bf66a1cd94b69259bfa26a47","observation_id":"70848a2d-af06-4274-9bfd-abf1ca090334","resolution":{"observed_at":"2026-08-01T05:23:59.591645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-01T05:23:59.693562Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.693562Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:99fd8ce1fa37891e358f37da7724a4f1bc2e6331956057b7f16176ecf15c2178","observation_id":"6699d1eb-4166-4100-acc5-dee7e338dbd5","resolution":{"observed_at":"2026-08-01T05:23:59.693562Z","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":"2026-08-01T05:23:59.777842Z","title":"Learningratefinder — pytorch lightning 2.0.7 documen- tation.https://lightning.ai/docs/pytorch/stable/api/lightning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.777842Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:e2a0b7a9e266257f15784e0ae32a9a16f409539971cf8c17cb9f660a6ca861a7","observation_id":"5b2c9028-aae8-474e-8343-9a0d14ccc24f","resolution":{"observed_at":"2026-08-01T05:23:59.777842Z","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":"2026-08-01T05:23:59.833144Z","title":"On the difficulty of training recurrent neural networks, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.833144Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:742f1b33e22e8b5a6079b2467f30d6d05487d74554949828e359d78ad016fdf3","observation_id":"6c110114-a438-47fa-bd02-4df10c54837e","resolution":{"observed_at":"2026-08-01T05:23:59.833144Z","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":"2026-08-01T05:23:59.937833Z","title":null,"venue":null,"work_id":null,"year":1952},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T05:23:59.937833Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:c7d356026025df6c182b051c3279a7c40b223818939913d84a0a89eff48f8830","observation_id":"5649cff5-0baf-489e-bcae-c6a27ff04f12","resolution":{"observed_at":"2026-08-01T05:23:59.937833Z","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":"2026-08-01T05:24:00.017371Z","title":"Pedregosa, G","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T05:24:00.017371Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:40dc4af089d95db18e9eb64e8e357adeda4e972a2c4ebddea013175481452509","observation_id":"424994c9-9873-4298-abb5-535d63324117","resolution":{"observed_at":"2026-08-01T05:24:00.017371Z","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":"2026-08-01T05:24:00.098276Z","title":"Unsu- pervised real-time anomaly detection for streaming data.Neurocomputing, 262:134–147, 11 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T05:24:00.098276Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:3280b578f56470a00e383375719402dd299a9ef215a47f1883458ac42780ce6e","observation_id":"7b8b5733-656b-4f4a-8a54-6a38acc36915","resolution":{"observed_at":"2026-08-01T05:24:00.098276Z","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":"2026-08-01T05:24:00.176259Z","title":"Constructing large-scale real-world benchmark datasets for aiops, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T05:24:00.176259Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:1912075ebfa74dcc3cc94acf3af83f98e69a5abba90af4869c7a07f112db0121","observation_id":"e9ed8728-1608-44da-afdd-42a530c9e953","resolution":{"observed_at":"2026-08-01T05:24:00.176259Z","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":"2026-08-01T05:24:00.227360Z","title":"Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T05:24:00.227360Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:e84ff38bd14a96b24d353bdda4bb02febe5ddcbf279fdfddae5333706db1c953","observation_id":"42ca27a2-0108-4151-8f82-d2d2fc357d6e","resolution":{"observed_at":"2026-08-01T05:24:00.227360Z","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":"2026-08-01T05:24:00.305591Z","title":"Anomaly Detection in Financial Time Series by Principal Component Anal- ysis and Neural Networks.Algorithms, 15(10):385, oct 19 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T05:24:00.305591Z"},"links":{"citing_paper":"/paper/2607.22786"},"observation_digest":"sha256:b74dc09d4a2bb042167dbab491db5ef0a88aab528994e07a7435cd3387679d4a","observation_id":"482adac8-2dd4-4be6-8c33-ab9e80537146","resolution":{"observed_at":"2026-08-01T05:24:00.305591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.22786","last_updated":"2026-07-24T12:09:19Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T02:46:47.074226Z","submitted_at":"2026-07-24T12:09:19Z","title":"Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":36},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.22786."}