{"as_of":"2026-08-10T14:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aa48aae1ebbb18cd0924cbe926f6e4d91f3b7568838428f8faaa697b0193d7b0","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:56:13.255398Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-17T04:55:36.263833Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-17T04:59:04.093241Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"cited_work":{"arxiv_id":"2507.12187","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12187","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","venue":null,"work_id":"95303657-a696-42c6-880a-1a500ab432c9","year":2025},"citing_paper":{"arxiv_id":"2511.21343","last_updated":"2026-05-06T13:39:27Z","snapshot_observed_at":"2026-07-06T22:37:03.716474Z","submitted_at":"2025-11-26T12:49:53Z","title":"Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-17T04:55:36.263833Z"},"links":{"cited_paper":"/paper/2507.12187","citing_paper":"/paper/2511.21343"},"observation_digest":"sha256:69cf5cf115a0b5ad40057c1afa87578d2cc18c52ea024c82fd69e26a21d49e45","observation_id":"7ca38a24-8683-4925-8c55-905d05d68a7f","resolution":{"observed_at":"2026-05-17T04:59:04.095795Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.12187/citation-record","integrity":"/paper/2507.12187/integrity","json":"/paper/2507.12187/citation-record.json","paper":"/paper/2507.12187"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.183554Z","title":"A survey of uncertainty in deep neural networks,","venue":null,"work_id":"76bc4721-c718-4b83-9334-3568510039e1","year":2023},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:12.566832Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:bffb72689a344f876119ceda106915f863c9e12c11f6bb6c67a9884539ec9016","observation_id":"bfca5dff-3d2b-4826-b876-43f488c9f66d","resolution":{"observed_at":"2026-08-06T16:56:14.189694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.06470","last_updated":"2021-07-18T16:17:28Z","snapshot_observed_at":"2026-08-10T02:30:14.599353Z","submitted_at":"2020-02-15T23:28:19Z","title":"Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.06470","snapshot_observed_at":"2026-08-06T16:56:12.713017Z","title":"Pitfalls of in-domain uncertainty estimation and ensembling in deep learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:12.713017Z"},"links":{"cited_paper":"/paper/2002.06470","citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:f0a91fa9e6461286272947475c09d6df8da271e545b5acb5d8bdd02d4a288c91","observation_id":"1dc0e85c-f58d-4f0b-83fe-6644a4a2d9c5","resolution":{"observed_at":"2026-08-06T16:56:12.713017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.165140Z","title":"Open set recognition through deep neural network uncertainty: Does out-of-distribution detection require generative classifiers?","venue":null,"work_id":"0f248e87-2b5e-408d-9bdc-f2cdcb5a2a0e","year":2019},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:12.841310Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:b79c04758303ee77f5de2a063054f356cd076d7c7044877bf2238a8692c8fe1b","observation_id":"e1a15fee-b04a-4995-9961-01a9c621e679","resolution":{"observed_at":"2026-08-06T16:56:14.169990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.148114Z","title":"An ensemble learning framework for anomaly detection in building energy consumption,","venue":null,"work_id":"047a901f-8df7-47b3-a042-0bd8c5989723","year":2017},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:12.995947Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:8884065d92ef63dbef6be98a97c1e726dc17c22dd9e768c4a9b30987a59cacb8","observation_id":"829596ac-31c0-4200-870f-777a3887f8de","resolution":{"observed_at":"2026-08-06T16:56:14.152919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.132990Z","title":"A novel ensemble learning approach to support building energy use prediction,","venue":null,"work_id":"9c5f1da2-c2ed-4284-8b0e-72925e9dd06d","year":2018},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.013612Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:b80f103a99dbdc94a8a06bd8e17435fce04f4593becf419ba2afe55be3e04f0a","observation_id":"0ff8b4c7-ae0d-4702-81ac-8b6cef9dfca9","resolution":{"observed_at":"2026-08-06T16:56:14.137619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.117542Z","title":"Bayesian multi-task learning MPC for robotic mobile manipulation,","venue":null,"work_id":"a1d65d08-2188-4684-abd8-4a905286747c","year":2023},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.018719Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:a2458c7e8f9e552a6afe2074f1af7db461d89d14bd3bba4ec3667326df336141","observation_id":"eda9319b-4764-42de-bb0b-ac7abaef8a18","resolution":{"observed_at":"2026-08-06T16:56:14.122345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.102138Z","title":"Learning-based on-track system identification for scaled autonomous racing in under a minute,","venue":null,"work_id":"2e808af4-2e17-4790-a7b1-cb19eedc43dd","year":2025},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.023906Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:f0d186b8146b493eb57021ab96bf63f5b411eeae2fe6c6cc07500bc50120a5b8","observation_id":"206f4c0e-160e-4fcb-8bec-56ad44042a55","resolution":{"observed_at":"2026-08-06T16:56:14.106569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.028913Z","title":"Continual lifelong learning with neural networks: A review,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.028913Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:4ba0edb9c2513ffb808e4da6ea89e2e62447e43eb0d9fc24c43bdc415c445cb2","observation_id":"5db34142-b0be-4108-9f89-7bf7bdceedbc","resolution":{"observed_at":"2026-08-06T16:56:13.028913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.075669Z","title":"Zhang and Y","venue":null,"work_id":"bca3c579-6566-435d-bc9a-077a3cf1813d","year":2012},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.033909Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:0447ee19ec6fbc11d2cd04e055642e50debf790bce8e7ee2b0ff148a5ce44fe5","observation_id":"3b064650-7c41-40c7-a935-ecac0d60db38","resolution":{"observed_at":"2026-08-06T16:56:14.080293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.038135Z","title":"Three types of incremental learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.038135Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:6deb44323c1f14893001fe9bfa49a45a675818a47f6641df7cdef79b2bc21d7f","observation_id":"7db18025-b806-4f36-9125-2cba86a0f3a3","resolution":{"observed_at":"2026-08-06T16:56:13.038135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.049718Z","title":"Explainable data-driven modeling via mixture of experts: Towards effective blending of gray and black-box models,","venue":null,"work_id":"9064fc57-7c92-4819-9e0f-4b8ef571d316","year":2025},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.042519Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:38f10a06403b4e76a4f3e985a9f25dfe624072bdf1706a1c6d734f333f7d8ff6","observation_id":"3b5b0ad0-7356-4730-b073-d64ad9b97369","resolution":{"observed_at":"2026-08-06T16:56:14.054185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-06T16:56:13.047216Z","title":"Theory on mixture-of-experts in continual learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.047216Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:1e4e9d81101310abc010251d89734a737d275b2b5d9752b909fd403dbaf3a851","observation_id":"3324e47c-1905-4177-bc58-b93b7f318d50","resolution":{"observed_at":"2026-08-06T16:56:13.047216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.032384Z","title":"A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US,","venue":null,"work_id":"64146b71-fb9e-4da4-ace1-7826c66e2845","year":2012},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.051910Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:f6c1eed0359126ee6fd72351bd31d5817fe473bf9c9d9bd7682a8edee7a2827c","observation_id":"fa28214e-b82f-4d46-8e00-48ee4851d57b","resolution":{"observed_at":"2026-08-06T16:56:14.038362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:14.015939Z","title":"Machine learning-based predictive control of nonlinear processes. Part I: theory,","venue":null,"work_id":"9484533c-3d0e-47fd-a2fe-61f8a25b8533","year":2019},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.056598Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:e3835cad2f8bcb11003cd5afc9b5c2f1b22318286bf7dd2d43bf97b06abaf6d6","observation_id":"a7c97bbf-c890-448b-ab31-6592c9c9a99e","resolution":{"observed_at":"2026-08-06T16:56:14.021417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.995411Z","title":"Coscl: Cooperation of small continual learners is stronger than a big one,","venue":null,"work_id":"16356855-cfe1-471e-85b9-653902f18381","year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.061826Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:c2f8187b00974686a8542494f09648d0b41046feef303d9d95c1806b404c83e0","observation_id":"f03e408f-8bcc-4b2e-8645-ea533e115a36","resolution":{"observed_at":"2026-08-06T16:56:14.000208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.066388Z","title":"Adaptive mixtures of local experts,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.066388Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:075d6129fd2c79ebc947210bc6d54a41e74c23b723e86d32ac51fc47cc25ec3d","observation_id":"020a5020-d857-4023-9c8d-b134ebc10fd5","resolution":{"observed_at":"2026-08-06T16:56:13.066388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.967341Z","title":"Hierarchical mixtures of experts and the EM algorithm,","venue":null,"work_id":"80138619-2c16-4850-b5da-6eda8b24930d","year":1994},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.071233Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:cd99bc0f7f3c0959a917f6c4e355f36bec46b1b02a14b4b42d9575aa4478ea50","observation_id":"844386dd-f64c-45f8-a8f8-ec4059aa8415","resolution":{"observed_at":"2026-08-06T16:56:13.972612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.946991Z","title":"Convergence results for the EM approach to mixtures of experts architectures,","venue":null,"work_id":"fd27f3c4-d157-49b7-bcc8-63c66ff903c5","year":1995},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.075932Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:63e53ae91c534e73e42352008602adbbe2d8a34ec9abfd9b5e9ed431e4ff3867","observation_id":"49ff12fd-ca10-41da-a9de-58243f930d2e","resolution":{"observed_at":"2026-08-06T16:56:13.952067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.930646Z","title":"A survey of ensemble learning: Concepts, algorithms, applications, and prospects,","venue":null,"work_id":"9b0500ba-e7ce-4ca9-b1b1-e9220c39daf6","year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.080825Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:968198a9026d64914b386b077eb8b586fe13ef860aa291eef2fda081b09ce39c","observation_id":"788de365-0d6c-4ba8-89d2-dc1d09089934","resolution":{"observed_at":"2026-08-06T16:56:13.935245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.915657Z","title":"Online learning: A comprehensive survey,","venue":null,"work_id":"b4811213-1e50-4f04-8f12-77fc0981ffd6","year":2021},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.085563Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:34fd49174e28ce6ad9f8c360b8cd4c4ad69d16c1dc2951b1ebecb7f92cb59799","observation_id":"220c519c-1684-4c84-ba90-1915420384f5","resolution":{"observed_at":"2026-08-06T16:56:13.920183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.089987Z","title":"Robust adaptive MPC using control contraction metrics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.089987Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:0b03c660e0389c41d0bfa169289b873349f1da927bc4b984709cd39560bc289e","observation_id":"9e349029-e9e0-4c48-accb-77b33e2611cb","resolution":{"observed_at":"2026-08-06T16:56:13.089987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.888262Z","title":"Robust MPC with recursive model update,","venue":null,"work_id":"5d848c2c-ffd9-4964-808f-ae54fe4f47df","year":2019},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.094091Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:64a33225b06e69e3907ab8b113fe400b81d9c0e67e31652fd9d620bbaf971078","observation_id":"1b685277-3aa3-4b9b-aa4d-682795f47fa1","resolution":{"observed_at":"2026-08-06T16:56:13.893181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.869942Z","title":"Adaptive model predictive safety certification for learning-based control,","venue":null,"work_id":"0ddec296-8db2-40a0-9dcc-a5bcc8f8c852","year":2021},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.098262Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:8700b27ddc8615a77f3c00bf8ce6ddff4b390979ab6ca2c1d2b0ca932f9e7d93","observation_id":"1d1c76b7-9d93-4887-83b7-ed032fa8514f","resolution":{"observed_at":"2026-08-06T16:56:13.876426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.850263Z","title":"ANN model adaptation algorithm based on extended Kalman filter applied to pH control using MPC,","venue":null,"work_id":"8eeeda81-5ecd-48a0-9fcd-5bc433c17457","year":2021},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.102190Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:a17bb2a336540ece1cd4b0a4f72edbcf92f66b959947cf9485bb33c8b954d2c9","observation_id":"7cbc558e-380e-48d2-bb34-059ac821d9b1","resolution":{"observed_at":"2026-08-06T16:56:13.856478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.834102Z","title":"Physics-informed online learning of gray-box models by moving horizon estimation,","venue":null,"work_id":"7b7e9bda-d9c7-4dd3-9d1f-464a029f939b","year":2023},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.106288Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:06cdec14bb3fda96357e88a42c9d30bd47ea733de3adf08b31c83b0970578be3","observation_id":"f1f0aab1-e967-49db-8bdb-9c28428fad08","resolution":{"observed_at":"2026-08-06T16:56:13.838780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.817464Z","title":"Towards lifelong learning of recurrent neural networks for control design,","venue":null,"work_id":"03c54726-acf2-4939-8a05-ec111a769288","year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.110433Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:a735648af78c66a2691a12a5903977543d6826a1746c4c65e4567449d205b54d","observation_id":"70d63ce5-a3c6-49ba-a034-d5ca48080fb6","resolution":{"observed_at":"2026-08-06T16:56:13.822934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.800836Z","title":"Hands-on bayesian neural networks—a tutorial for deep learning users,","venue":null,"work_id":"0dccfff0-f24a-4e1f-b87e-0d98d84e795e","year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.114478Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:e5f45a5130ba7def0bd68cdcdb89a9ef83c7173e72d6082ea94829e7f09eeec5","observation_id":"1a1ac247-56b5-4975-ab74-eea7ab14a2b1","resolution":{"observed_at":"2026-08-06T16:56:13.805721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.784977Z","title":"Meta-learning priors for efficient online bayesian regression,","venue":null,"work_id":"62e09ddd-4f67-42cb-8ae0-13256dd0053f","year":2018},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.120049Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:b50787684ef43e135897b686e86f90d250f455532771fa4ee5a7c6da9628d833","observation_id":"2a0a3b34-795d-4b01-8979-6a0444d6dc0a","resolution":{"observed_at":"2026-08-06T16:56:13.790062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.764667Z","title":"Bayesian layers: A module for neural network uncertainty,","venue":null,"work_id":"92d146c1-87c9-4cdc-9768-bdc1fdd33b6e","year":2019},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.124310Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:936f754db2908ddc6f857b2059cf2008ec6715fff9d66f3b89c5bdc16e33d0be","observation_id":"8293e6cc-fb9f-4606-b89c-3fd1b9053ca0","resolution":{"observed_at":"2026-08-06T16:56:13.771602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02310","last_updated":"2025-02-04T13:28:52Z","snapshot_observed_at":"2026-08-09T12:32:40.777101Z","submitted_at":"2025-02-04T13:28:52Z","title":"Gaussian processes for dynamics learning in model predictive control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02310","snapshot_observed_at":"2026-08-06T16:56:13.128468Z","title":"Gaussian processes for dynamics learning in model predictive control,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.128468Z"},"links":{"cited_paper":"/paper/2502.02310","citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:743267a01e69b114fa940bbe0ac2a695d47cc3a35c40a01558afa7f54ed36498","observation_id":"ea3ebc19-22ea-4072-8f3c-1ed567923345","resolution":{"observed_at":"2026-08-06T16:56:13.128468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.747046Z","title":"Online learning-based model predictive control with Gaussian process models and stability guarantees,","venue":null,"work_id":"55e5b3bd-bbe0-48d4-9029-f07db84afae5","year":2021},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.133624Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:1e7048912adbb699a5bdd1a9c7d032b35974f1f5f26fbb654ceea5f51e93ec17","observation_id":"8d6cd735-d5be-44a7-bc25-73620fa1f242","resolution":{"observed_at":"2026-08-06T16:56:13.752103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.731900Z","title":"Learning-based model predictive control: Toward safe learning in control,","venue":null,"work_id":"2a5ce1cf-6fcd-45d1-b6ff-26ccebee9701","year":2020},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.138298Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:49b319d2012da46313ed065b269a40098a9f6041f4d24533869484aeaf250943","observation_id":"8864db9f-414b-481c-92fa-012ea48678c9","resolution":{"observed_at":"2026-08-06T16:56:13.736800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.714787Z","title":"Cautious model predictive control using gaussian process regression,","venue":null,"work_id":"6843ee84-ad8e-426c-a6fd-181172f81b49","year":2019},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.143597Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:efa648f793f9f511189caf3b11a00df4014640f3108fd1bcf52e6a128818d796","observation_id":"5b5094bd-7eb1-4ce9-b28a-9242875e30cf","resolution":{"observed_at":"2026-08-06T16:56:13.719922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.699881Z","title":"Contextual tuning of model predictive control for autonomous racing,","venue":null,"work_id":"bf7a85bb-04cf-4782-810b-ef3d66f34af3","year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.148460Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:9e5a27eea4b536cb06b67acc72d1741dbfc1f98b22c0aa4cedce398d05e0ef78","observation_id":"fa3c63db-e750-4627-9285-4675328ad3d7","resolution":{"observed_at":"2026-08-06T16:56:13.704462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.683684Z","title":"Online learning of MPC for autonomous racing,","venue":null,"work_id":"6ccfca6a-cea0-4cf5-9bc3-deeded08c058","year":2023},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.152560Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:9aa49a40c6b9a40342d6533c573167870b21942cd10cdbde24f9df597460fbc6","observation_id":"66f96689-b811-41c1-8d92-13f41cc4c67f","resolution":{"observed_at":"2026-08-06T16:56:13.688170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.667764Z","title":"Thinking, fast and slow,","venue":null,"work_id":"01b3d49d-6d39-4331-b439-b60bdcfb5b14","year":2011},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.157101Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:b455fe3b0bb1531d984b50906020bafd17af8c174560685545f0bafcac79f22b","observation_id":"99f1281e-e4ab-4a28-bfcc-3f0dcc3f5387","resolution":{"observed_at":"2026-08-06T16:56:13.672615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.650483Z","title":"Thinking, Fast and Slow by Daniel Kahneman,","venue":null,"work_id":"bfb1e4dc-ebe0-4219-a77a-0c7afb496273","year":2025},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.162070Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:87782ca1d911d6c36bc2f50123afefb19e8bdd45d4056d369433618209bba422","observation_id":"25e3de15-f6db-4111-84e2-c41a457db8eb","resolution":{"observed_at":"2026-08-06T16:56:13.656213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.634610Z","title":null,"venue":null,"work_id":"29d27022-7837-46cf-9d20-a3999ba267ef","year":2009},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.168401Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:34d407dc27c7f9f94a57ecea51fbdc8902bd7c233d2890bfc9497f0e18acc20d","observation_id":"a9c700ba-a3a9-4e93-be7b-425e026c7a55","resolution":{"observed_at":"2026-08-06T16:56:13.639013Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.618183Z","title":"Nonlinear optimization of district heating networks,","venue":null,"work_id":"d08ef615-0535-4d09-a953-514d42b81ec1","year":2021},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.172509Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:2997555cc57ab9149b40d15d5f05444b5d687353cf0521aed93c62db52639fbb","observation_id":"d4ca504f-4a9a-4421-89bc-fd9ebc1fd364","resolution":{"observed_at":"2026-08-06T16:56:13.623291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.601645Z","title":"Lifelong learning for monitoring and adaptation of data-based dynamical models: a statistical process control approach,","venue":null,"work_id":"f5512827-ed5e-4cbb-81af-8c5615dc7459","year":2024},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.179255Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:089d87bd53911c34a4e067cc4aaf9fe7bc64924c30e313ce4449e95e80efd934","observation_id":"0cea00ec-deeb-41f7-bf64-b9022b404392","resolution":{"observed_at":"2026-08-06T16:56:13.606757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.586535Z","title":"Machine thinking, fast and slow,","venue":null,"work_id":"a06707c5-4e75-496d-a62d-e49a5fa812d4","year":2020},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.183779Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:766e2638f91a144eeca5a61c157f814bdf166d76c1599b25d2f5b491703439b8","observation_id":"16596358-1afb-4097-91b2-ec82bb22dfe3","resolution":{"observed_at":"2026-08-06T16:56:13.591296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.569604Z","title":"Thinking fast and slow in AI,","venue":null,"work_id":"078b7533-90bb-439b-9014-8bd63b7c07d0","year":2021},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.187989Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:27622d20afac4867068e7a563e194ebfb63406aef20edec0ade9e774ccc44c6a","observation_id":"efad5be0-67b1-41ba-8f68-f1cc92ae822f","resolution":{"observed_at":"2026-08-06T16:56:13.575020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.551015Z","title":"Thinking fast and slow with deep learning and tree search,","venue":null,"work_id":"8e10cde3-f395-4f88-9838-138e90df7d8e","year":2017},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.192394Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:0e521a79e4b263a6c73ab05e305ca617546098097eeba5f47b694e875b091a76","observation_id":"9e88dd30-aa6e-4250-b80d-955a41ca84a7","resolution":{"observed_at":"2026-08-06T16:56:13.556100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.535596Z","title":"McShane-Vaughn, The Probability Handbook","venue":null,"work_id":"5343c6d6-ed19-4023-91d4-3697a90fa078","year":2016},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.197924Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:68154eedd47f15d2bc748619d991048744b02cea9528a8428f86551fae7482e0","observation_id":"48573323-e993-49fb-a8a2-4134ed513370","resolution":{"observed_at":"2026-08-06T16:56:13.540395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.202897Z","title":"Model predictive control: Theory and practice—a survey,","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.202897Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:1b87a8594e8db175e5f5a62c27514b7bf8fea44eaea2265eacd4daa99f8e30d4","observation_id":"287edcb5-ad22-43c9-bd6e-9089efed4cf6","resolution":{"observed_at":"2026-08-06T16:56:13.202897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.508235Z","title":"On the certainty equivalence principle and the optimal control of partially observed dynamic games,","venue":null,"work_id":"61214abb-719e-425d-9af3-2642a77d779a","year":1994},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.210502Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:bb3e7bf27f4361be7f99f15b51d90457d22bfba5b8266ef1fab0a5b2d64fcd6d","observation_id":"eee2faa5-51f6-4123-8f23-84d3bfe3e8ef","resolution":{"observed_at":"2026-08-06T16:56:13.513164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.492405Z","title":"On recurrent neural networks for learning-based control: recent results and ideas for future developments,","venue":null,"work_id":"5d7bdca8-186c-48d6-a3f3-f2dedce537a7","year":2022},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.215256Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:7770cbc50ec2bfa460b4efa1845c2b2e8243aaa38abef6d5f2a826f3794203d4","observation_id":"2da1437d-e708-4428-8348-6079bc82de02","resolution":{"observed_at":"2026-08-06T16:56:13.496827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.477938Z","title":"Learning stable gaussian process state space models,","venue":null,"work_id":"c46bee79-b806-4eca-a975-ced8916c817f","year":2017},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.220009Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:dc208d9712fdbbaa231f6b5fcc2bc4406bcf61cdb071a37e6c9500878f8da906","observation_id":"17a66d54-e34a-4cc5-82fa-0bbde230ac98","resolution":{"observed_at":"2026-08-06T16:56:13.482388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.462182Z","title":"Using the Nystr ¨om method to speed up kernel machines,","venue":null,"work_id":"f610e2f5-dec6-4862-b993-5044ac5154af","year":2000},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.224342Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:bb0c0df53e07499d2fb6267e84ac7934685076aecdd911bd58930d190602467f","observation_id":"5b3da1b3-9f11-4cfe-89bd-80e77ebb44d3","resolution":{"observed_at":"2026-08-06T16:56:13.466587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.442844Z","title":"Sparse spectrum gaussian process regression,","venue":null,"work_id":"410733a5-dcf7-4cac-b4a9-dc561ff62c15","year":2010},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.228843Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:4c4cecc7aa2038358dbd7276674443a0f5683a1fda23c884267fa336156e95f0","observation_id":"263b9f6f-92d1-45dc-b94f-3881afbe87d5","resolution":{"observed_at":"2026-08-06T16:56:13.447617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.427143Z","title":"Multivariable feedback design: Concepts for a classical/modern synthesis,","venue":null,"work_id":"c27c652f-9bfb-40da-b94d-3c23861ca6cc","year":1981},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.232976Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:fd2697de87c8342a827f3ee32c131394e67554791aaef7238c16cf4a3cd198cf","observation_id":"7944c2e0-3923-4e9f-b5cd-059b5df44956","resolution":{"observed_at":"2026-08-06T16:56:13.431691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.412177Z","title":"Heat roadmap europe 4: quantifying the impact of low-carbon heating and cooling roadmaps,","venue":null,"work_id":"eea8b34f-fe7c-4c58-b8bd-a94e5582ed74","year":2018},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.237941Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:2b0279103fe7a4a61708f6ed6522f3a98c5e05df35ea2933d447945e23ca8991","observation_id":"f69f8bee-9f7f-4c54-a6f9-fa1993e5889e","resolution":{"observed_at":"2026-08-06T16:56:13.417050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.396085Z","title":"Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study,","venue":null,"work_id":"473656e8-ea6b-4abc-a541-b234856b5d3b","year":2023},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.243124Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:3a13d7013d9b7750e7a5bf4cdfaa87f4e50245d2ef2140682ea0c7b09f40319c","observation_id":"feb5d8b5-74f1-401c-ad6a-aa30c6d456cb","resolution":{"observed_at":"2026-08-06T16:56:13.400678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.375344Z","title":"Development and experimental validation of an open-source model library for district heating network simulation,","venue":null,"work_id":"68f19504-0da8-4ec9-9bf0-ce699864cac3","year":2024},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.249335Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:a1841825980e530df6b95ea65cdf03633993b74f94126187a65554f2fbcfd4d0","observation_id":"9ffb86cd-2a80-4e53-9de0-22289848d58c","resolution":{"observed_at":"2026-08-06T16:56:13.380907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:56:13.355864Z","title":"Physics-informed neural network modeling and predictive control of district heating systems,","venue":null,"work_id":"ca0d95ad-6b75-47b9-b6e7-c1320931d528","year":2024},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.255398Z"},"links":{"citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:77ae5634810567825c8be5f1e047e95bd9a1f05d990286bd313163e49cccafda","observation_id":"8ec6eff5-9a9e-47e4-afb2-c574b09c4a4f","resolution":{"observed_at":"2026-08-06T16:56:13.362533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-10T06:46:30.767984Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":46},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+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 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2507.12187."}