{"as_of":"2026-08-17T19:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45e87a324726d2e731f90ceea594f0a617df7bcccc7bb4cb41ad8c910291d546","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:42:36.476148Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2412.00070/citation-record","integrity":"/paper/2412.00070/integrity","json":"/paper/2412.00070/citation-record.json","paper":"/paper/2412.00070"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:42:36.809992Z","title":"A survey on deep learning for data-driven soft sensors,","venue":null,"work_id":"520e093b-1664-4667-996d-62801a319600","year":2021},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.369581Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:096976d5a39f781de2823809938b11e144bf5011fdc8c1729aeef63e383c3a6d","observation_id":"f15c6c1d-37bc-4193-8843-4c987fefc3ea","resolution":{"observed_at":"2026-08-12T12:42:36.813548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.799710Z","title":"Fuzzy adaptive knowledge-based inference neural networks: design and anal- ysis,","venue":null,"work_id":"54a943a2-4ea8-4d41-87b3-0a3ed1376f0b","year":2024},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.374304Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:b2d0c4f24332bf67b02fb132d902706b47764dddcefafb9fbfd17a5020184f62","observation_id":"6375ae93-ed08-435b-b96d-78fbd3262f48","resolution":{"observed_at":"2026-08-12T12:42:36.803168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.788820Z","title":"A learning con- volutional neural network approach for network robustness prediction,","venue":null,"work_id":"85b1951a-a330-44c7-b279-9bf9a96b5fbf","year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.378079Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:7e21bef24bc883e2cb0769cb4e713ff3f5517c0f2b128212253c110885e97300","observation_id":"e3453319-01dd-4ac2-8c5e-6001a1a55ad7","resolution":{"observed_at":"2026-08-12T12:42:36.792725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.777681Z","title":"Identification of nonlinear output-affine systems using an orthogonal least squares algorithm,","venue":null,"work_id":"a2dde3b2-e9a2-4237-a295-1d136f6c7567","year":1988},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.381767Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:cf000175ac6000f798914916e7924b0f5514d8d18a1336dde686521987724531","observation_id":"4ac3a78d-a892-416e-aa5d-71286b26854d","resolution":{"observed_at":"2026-08-12T12:42:36.781675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.756783Z","title":"Ensemble stochastic configuration networks for estimating prediction intervals: a simultaneous robust training algorithm and its application,","venue":null,"work_id":"9f9a26f1-06cc-40b7-96f0-8595fcb291fd","year":2020},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.389615Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:392c8bc9507743b6fb854223972220764186972e8a49d9261f0272940aef852b","observation_id":"17e38181-fcfa-43f7-abe3-56061d80877e","resolution":{"observed_at":"2026-08-12T12:42:36.760423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11461","last_updated":"2023-04-22T18:22:10Z","snapshot_observed_at":"2026-08-16T15:38:29.191390Z","submitted_at":"2023-04-22T18:22:10Z","title":"Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11461","snapshot_observed_at":"2026-08-12T12:42:36.393505Z","title":"Recurrent neural networks and long shortterm memory networks: Tutorial and survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.393505Z"},"links":{"cited_paper":"/paper/2304.11461","citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:1715ac51e3f639224cf89a21739c80a67fea2f6dca771e81b6987ffe48f6b250","observation_id":"7b420ab6-7d83-414d-b95f-d5124b8eef9a","resolution":{"observed_at":"2026-08-12T12:42:36.393505Z","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":"2026-08-12T12:42:36.745758Z","title":"Hybrid recurrent neural network architecture-based intention recognition for human–robot col- laboration,","venue":null,"work_id":"ac39d5c3-ca0e-4781-ab4e-e5329a3f0784","year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.397203Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:2fde2a68ae5a2ee956b6eaf5c1aba68cb7c0ab798b290f3590b162158cfea07d","observation_id":"63a61299-555b-4621-935a-f82ada8c33b8","resolution":{"observed_at":"2026-08-12T12:42:36.749687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.734144Z","title":"Recurrent neural network training with convex loss and regularization functions by extended kalman filtering,","venue":null,"work_id":"6333c858-30eb-430e-b6f3-4962f1ab1801","year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.400620Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:45dccb2c48850a986374653a2b031296016c587f3148d51d917bdb963ac2a011","observation_id":"e5aacb8a-2b50-4e97-bbf5-2c5041a247f2","resolution":{"observed_at":"2026-08-12T12:42:36.738693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.404131Z","title":"Functional-link net computing: Theory, system architecture, and functionalities,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.404131Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:093ce9f1b70e74802a77c1e28245e0db985d98e7fc14b41f5ebe0d3329edcb05","observation_id":"fdaf9a81-8c6a-43eb-b018-9ec54cbe8da2","resolution":{"observed_at":"2026-08-12T12:42:36.404131Z","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":"2026-08-12T12:42:36.716233Z","title":"The echo state approach to analysing and training recurrent neural networks-with an erratum note,","venue":null,"work_id":"abcd9d3b-a5d8-44b3-84b1-e9541e92a23f","year":2001},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.407545Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:f9d79e4d75e97c91229f863077b3c26b6c7c878fce27027b9fb316010c840dde","observation_id":"39a0b751-39ca-4854-bd8c-07f9e65f3dad","resolution":{"observed_at":"2026-08-12T12:42:36.721023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.705366Z","title":"Randomness in neural networks: an overview,","venue":null,"work_id":"2eca1ed5-1896-455c-9f9a-65e39fac8260","year":2017},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.410854Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:309fc30705dedacd04e2348b0ae274052c4e074c9c249c09bc2265362bada02f","observation_id":"a0e3b534-e7b5-4fd1-8fa9-a5efb3bd2e73","resolution":{"observed_at":"2026-08-12T12:42:36.709329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.694751Z","title":"Editorial: Randomized algorithms for training neural net- works,","venue":null,"work_id":"7a19f085-e498-4774-aea0-0d652d5c8d36","year":2016},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.414544Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:9419ed030daa56c38f4ff9cafce4b5b7d47871416f1f9cb70addfc7e3c987cd5","observation_id":"d42390de-415b-44df-bee4-e926d4e32a98","resolution":{"observed_at":"2026-08-12T12:42:36.698359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.683846Z","title":"Optimization and applications of echo state networks with leaky-integrator neurons,","venue":null,"work_id":"bad2e094-459e-4fb9-89e5-36b36849495f","year":2007},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.418110Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:b3986875fc7975185df8dd0af8f71ad281fab98d57813b1a1a4be34c60561b2a","observation_id":"df156eee-ba3d-4c50-9e7d-92ce2e900e06","resolution":{"observed_at":"2026-08-12T12:42:36.687723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.672507Z","title":"Pruning and regularization in reservoir computing,","venue":null,"work_id":"852a531e-4576-48b3-9536-9154c9d38b2f","year":2009},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.421471Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:be049a370d53646a80891fcc1d4c88e263cb453a0c94d849cbfe7c1c0dfd8bfb","observation_id":"611d3f6f-421c-498c-b48d-f086792e7324","resolution":{"observed_at":"2026-08-12T12:42:36.676174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.661058Z","title":"Dynamical regularized echo state network for time series prediction,","venue":null,"work_id":"5e3f9834-88dc-4e39-a878-dc9653c0767c","year":2018},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.424860Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:11a2dab814eb2cd209c0fb6b352c983faec60dc7fdc1962500ccb929e7ce051f","observation_id":"53cf1859-bfdc-4b4c-bc1f-886d726e2771","resolution":{"observed_at":"2026-08-12T12:42:36.665786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.650719Z","title":"A decentralized training algorithm for echo state networks in distributed big data applications,","venue":null,"work_id":"1f96cf30-2f21-4121-9130-fc3ab7d9764f","year":2016},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.429882Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:f88ae6e5602d7c672af67391f1dc6a6c132da1672f8c8d2d66eb0b5907ffab38","observation_id":"a54d4980-ea5e-492b-821f-2333cd613251","resolution":{"observed_at":"2026-08-12T12:42:36.654690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.640766Z","title":"Stochastic configuration networks: Fundamentals and algorithms,","venue":null,"work_id":"919c3d9b-52de-4e54-a1d4-f2bc5fc84941","year":2017},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.433227Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:589fb42ce4beca1f1183450dcdf87134ef7393d467298d18ffff4237f8da6f9d","observation_id":"d84c63ab-8631-4642-808c-65244f5aa461","resolution":{"observed_at":"2026-08-12T12:42:36.644297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.630389Z","title":"Multitarget stochastic config- uration network and applications,","venue":null,"work_id":"4dba3298-2372-4128-b820-107654d5ca4d","year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.436781Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:091e6e7eee2d792322f0748aa48e5701abbddcffbc062f82eee17cd3feaecfed","observation_id":"5bcc00b2-4418-45b8-8c7f-a97bc66a8368","resolution":{"observed_at":"2026-08-12T12:42:36.633904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.619994Z","title":"A sparse learning method for SCN soft measurement model,","venue":null,"work_id":"4259de5d-fcff-4c9c-bd2a-21299bd8ba0c","year":2024},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.440343Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:0d05a65b213240ae750320d7fb470f3c657c08dd7d8aceb48c6496779ae56cf8","observation_id":"114cc11f-90f4-4540-bdc2-74796f8e3e6a","resolution":{"observed_at":"2026-08-12T12:42:36.623631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.609588Z","title":"A regularized stochastic configuration network based on weighted mean of vectors for regres- sion,","venue":null,"work_id":"6dda8518-0901-4d85-93ef-7afa166d0bf3","year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.444115Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:130527c20887557f5b0bce04d6d3ddae52a23d5c6191d1663f1bf8e383b3c391","observation_id":"cbce17b7-2bce-406f-a56e-373d1d61b27e","resolution":{"observed_at":"2026-08-12T12:42:36.613178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16959","last_updated":"2025-04-02T02:12:52Z","snapshot_observed_at":"2026-08-16T13:40:52.809439Z","submitted_at":"2024-06-21T03:21:22Z","title":"Recurrent Stochastic Configuration Networks for Temporal Data Analytics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16959","snapshot_observed_at":"2026-08-12T12:42:36.447783Z","title":"Recurrent stochastic configuration networks for temporal data analytics,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.447783Z"},"links":{"cited_paper":"/paper/2406.16959","citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:1fb509108bc5b2821a8077f2a228bb8b2ad96244c0814b75f0f5ff8b87a1810e","observation_id":"08ec9d29-c93f-4ea6-98a4-66fc8bd47167","resolution":{"observed_at":"2026-08-12T12:42:36.447783Z","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":"2026-08-12T12:42:36.767220Z","title":"Time-varying input and state delay compensation for uncertain nonlinear systems,","venue":null,"work_id":"9c82b8c9-cb0f-4630-ab6e-d03959e040a8","year":2016},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.451613Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:cd7c3fd81998ac5b7b4b597d7575f9aa49c798663121c67b8eedd9df0362d4c8","observation_id":"3c72bde1-4a7d-43b1-8f58-fd450861bd2a","resolution":{"observed_at":"2026-08-12T12:42:36.770749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.598638Z","title":"Dynamic gain reduced-order observer- based global adaptive neural-network tracking control for nonlinear time-delay systems,","venue":null,"work_id":"edecb67f-cdb0-4220-8eaa-573cc7428ce1","year":2023},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.454856Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:de4b08f0d8e1ba77ac8391d01b6e783e346a80d598eab469ad63a315e900f51e","observation_id":"eccc9e93-eadd-4820-a29d-edde0a2b420b","resolution":{"observed_at":"2026-08-12T12:42:36.602692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.587231Z","title":"Regression shrinkage and selection via the lasso,","venue":null,"work_id":"995d2a33-b2c9-4475-9823-7e1449955f58","year":1996},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.458308Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:98ea6e350c537e97a245b8c86af37a5f17753215831389363de90d2dbb112242","observation_id":"2a1ee500-b836-4d7e-a6f2-34b90191fc84","resolution":{"observed_at":"2026-08-12T12:42:36.590731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.577263Z","title":"Regularization and variable selection via the elastic net,","venue":null,"work_id":"7bb98cd1-6eb0-42d7-84f9-3ac88156cc60","year":2005},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.461540Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:b741c0df596befa72af5c05240da3c8f74ccb394a10feba3eaa1d140f2315114","observation_id":"0199dcb9-98f8-48e2-ad5c-9f7625026269","resolution":{"observed_at":"2026-08-12T12:42:36.580810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.566648Z","title":"Predicting particle size of copper ore grinding with stochastic configuration networks,","venue":null,"work_id":"0d49f7f9-2b10-4afd-be6c-3df2772d9e42","year":2024},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.464848Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:b6c95135f5f3b9920cc39f95368efe21ec53b3d72403b0490e563ac7b3dc7af9","observation_id":"2023b106-19d0-4e8e-956b-a455c066762a","resolution":{"observed_at":"2026-08-12T12:42:36.570522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.556196Z","title":"Adaptive filtering prediction and control,","venue":null,"work_id":"565a6dc0-10bd-4727-ad09-21bd6a3087bc","year":2014},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.468570Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:103dd3025573fa97ecda344244ebb99372155f4f95ff697158462b3b50add9f0","observation_id":"14ad9293-40cb-4d10-806c-87c1b1bb58f7","resolution":{"observed_at":"2026-08-12T12:42:36.559929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:42:36.544745Z","title":"Soft sensors for product quality monitoring in debutanizer distillation columns,","venue":null,"work_id":"14e95d1f-a8c2-473a-83ab-78dc6387b096","year":2005},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.472702Z"},"links":{"citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:947dbfce253ffc598aa4bee2eea00c371dc7eb43a2a753eb1e0619be2ee01044","observation_id":"d7618faa-1080-4bdb-99e9-e09b2475fa87","resolution":{"observed_at":"2026-08-12T12:42:36.548629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.11303","last_updated":"2024-11-18T05:58:47Z","snapshot_observed_at":"2026-08-13T19:39:07.783941Z","submitted_at":"2024-11-18T05:58:47Z","title":"Recurrent Stochastic Configuration Networks with Incremental Blocks","version":1},"cited_work":{"arxiv_id":"2411.11303","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.11303","snapshot_observed_at":"2026-08-12T12:42:36.507199Z","title":"Recurrent Stochastic Configuration Networks with Incremental Blocks","venue":"cs.LG","work_id":"e7f644bc-3be3-44f5-a717-43aac7868653","year":2024},"citing_paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:42:36.476148Z"},"links":{"cited_paper":"/paper/2411.11303","citing_paper":"/paper/2412.00070"},"observation_digest":"sha256:b91e6d061c8792fb6a3a7547e2c02652047e839e03d43ce6c79980170d07a2a9","observation_id":"cb1f6e21-1823-4d82-894e-8e1db19aabd5","resolution":{"observed_at":"2026-08-12T12:42:36.513085Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.00070","last_updated":"2024-11-26T03:06:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T06:41:31.168823Z","submitted_at":"2024-11-26T03:06:39Z","title":"Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":25},"total_outbound_references":29},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.00070."}