{"as_of":"2026-08-17T08:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:185e5eee2db77c62ffc78897148b53064fd70278c6b67d0da83eb418f7932d3e","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:18:40.669871Z","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":"2026-08-17T06:30:58.91139+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/2412.02758/citation-record","integrity":"/paper/2412.02758/integrity","json":"/paper/2412.02758/citation-record.json","paper":"/paper/2412.02758"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:18:39.308049Z","title":"From model-based control to data-driven control: Survey, classification and perspective,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.308049Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:2660aa644cba52abe0c77f6ed111914d9af10add735e14f4526b24a743e99cd4","observation_id":"39cf9757-76fc-4ebf-87b7-0ded63b80038","resolution":{"observed_at":"2026-08-11T23:18:39.308049Z","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":"2026-08-11T23:18:43.922534Z","title":"On an iterative technique for Riccati equation com- putations,","venue":null,"work_id":"f518612c-38c8-407b-8f80-16a1c3deb8c2","year":1968},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.353481Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:9f28a38df479d4a5233729223135c85b7c05660a5e52ef62c3aee80854d5f69d","observation_id":"2717bf6f-0ada-4151-bcd6-c2e144545f25","resolution":{"observed_at":"2026-08-11T23:18:43.927970Z","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":"2026-08-11T23:18:43.754668Z","title":"Online optimal tracking control of continuous-time linear systems with unknown dynamics by using adaptive dynamic programming,","venue":null,"work_id":"0c8538ea-8696-4fbe-b544-dd7758d9e13f","year":2014},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.438398Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:87046cb26530f92ce089160faa3ef90c8e01878b0b4572a706b8c4c5b6c3bcb2","observation_id":"264379a3-decd-49ee-af76-1f8b59222502","resolution":{"observed_at":"2026-08-11T23:18:43.832863Z","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":"2026-08-11T23:18:43.604884Z","title":"Optimal output-feedback control of unknown continuous-time linear systems using off-policy reinforcement learning,","venue":null,"work_id":"9a7e543b-c3be-44aa-a934-1ee86fd31b40","year":2016},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.442709Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:ffa2c0982611431d140c131fb3fa3dc4d4c2bd2b0880e4f671ecf55d158d2075","observation_id":"0ccb182e-3454-4eac-a40c-0a647c105a68","resolution":{"observed_at":"2026-08-11T23:18:43.659327Z","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":"2026-08-11T23:18:43.593982Z","title":"Data-driven finite-horizon optimal control for linear time-varying discrete-time systems,","venue":null,"work_id":"1fcd8f75-8591-438c-b002-36591f7f34eb","year":2018},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.446569Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:f397dddf51082f713267fb16cd3509f261f7ff59d199d0b51712be47a9014ac4","observation_id":"fdce4efd-fc99-4ac2-9714-85a1790ed5d1","resolution":{"observed_at":"2026-08-11T23:18:43.597703Z","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":"2026-08-11T23:18:43.580155Z","title":"Finite-time analysis of approximate policy iteration for the linear quadratic regulator,","venue":null,"work_id":"b5d1a639-1d8b-46a4-8211-39390ebbd714","year":2019},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.490315Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:4a18c460ead2d0ed07ae43e46ceaa78d28d86b634ba1fb5da8cfd139c736fc85","observation_id":"eb9b2440-c0c7-4c35-9426-9d9902536cc6","resolution":{"observed_at":"2026-08-11T23:18:43.585382Z","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":"2026-08-11T23:18:43.567711Z","title":"Robust policy iteration for continuous- time linear quadratic regulation,","venue":null,"work_id":"57d9387d-1257-4138-a567-e099cc75ca16","year":2021},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.559912Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:e36ef9c7f48c681a913a31be16df333e36f52bc23ec439de0e506716db8cb30b","observation_id":"fc3764fb-487f-47fe-99a5-e4b86c8f9993","resolution":{"observed_at":"2026-08-11T23:18:43.572150Z","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":"2026-08-11T23:18:43.555989Z","title":"Efficient off-policy Q- learning for data-based discrete-time LQR problems,","venue":null,"work_id":"2210fae4-42be-4629-90aa-0006b529f9fd","year":2023},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.640316Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:1de47a48196c394ff46cef5b3d84e2dd2c64ac5c716a3b8c035dd8c56623c2a7","observation_id":"2665eac5-e63d-45a2-9328-140c1988b03f","resolution":{"observed_at":"2026-08-11T23:18:43.560085Z","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":"2026-08-11T23:18:43.544654Z","title":"Safely learning to control the constrained linear quadratic regulator,","venue":null,"work_id":"d4a7c24d-a16d-47c7-ab9e-9311414162f7","year":2019},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.734549Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:ae33ae94bdfd8dd6bd09692a466c42e0f280499a549c96ab2b328519d6e589db","observation_id":"63ce0536-6dac-4a53-a4f0-60a4843baf8b","resolution":{"observed_at":"2026-08-11T23:18:43.548471Z","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":"2026-08-11T23:18:43.497127Z","title":"Certainty equivalence is efficient for linear quadratic control,","venue":null,"work_id":"9ced3dfa-26a0-4057-aa93-6273e0074cf2","year":2019},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.795508Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:197dba739f47c2155b7883091bf953f7f9f4d3cd2007284fdcb30b02bebe6448","observation_id":"66cf4f9a-34b1-42ee-a8a6-e2843d1a5f38","resolution":{"observed_at":"2026-08-11T23:18:43.533733Z","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":"2026-08-11T23:18:43.364132Z","title":"Learning robust LQ-controllers using application oriented exploration,","venue":null,"work_id":"eae07e8a-c2b7-4eea-8b5c-f5c7aae5c84f","year":2019},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.814731Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:a71232a767c50a09c2fcf92c33ef186f5820b4f26ae1e34a3dea8b5e2f67bf51","observation_id":"c17262a0-2b77-457d-9e9e-43b5c06eb1b6","resolution":{"observed_at":"2026-08-11T23:18:43.442229Z","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":"2026-08-11T23:18:43.198403Z","title":"Formulas for data-driven control: Stabilization, optimality, and robustness,","venue":null,"work_id":"a093ace6-57df-41c3-96cd-b3393c95579b","year":2019},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.834433Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:f90e7b7404b59ff513fe0a3036a8dd50607a8a5c135b4deb4a2cb38b7ce26f07","observation_id":"7c6654b9-97e4-4c4c-a0a8-7ca41c8b96f0","resolution":{"observed_at":"2026-08-11T23:18:43.272539Z","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":"2026-08-11T23:18:43.183963Z","title":"Data informativity: a new perspective on data-driven analysis and control,","venue":null,"work_id":"34ddddaf-4180-4d40-a484-57188e3feeb4","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.854674Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:0d9023cba1478b7264b9af9302411901ae9c367742feb2421e8afea9d28727c4","observation_id":"1a0e482d-60ea-479b-8282-5f655abdba54","resolution":{"observed_at":"2026-08-11T23:18:43.188734Z","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":"2026-08-11T23:18:43.171477Z","title":"Data-driven linear quadratic regulation via semidefinite programming,","venue":null,"work_id":"9f095b75-5e35-4e35-ad16-b9bb47606cb9","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.858752Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:c06fe6552ab01a948ea607feba9d7b3b81447bcdc6711f63e78857ab6c29908b","observation_id":"7a4322ac-6111-405a-916a-eb9f36e3bd48","resolution":{"observed_at":"2026-08-11T23:18:43.175031Z","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":"2026-08-11T23:18:43.157001Z","title":"Online learning of data-driven controllers for unknown switched linear systems,","venue":null,"work_id":"12f311f7-0546-4b58-a9ea-c5e2645ed98f","year":2022},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.863744Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:8babab18d44b30bf5ee684edc8c47df59856563637691549d01f07233b78d347","observation_id":"3278fc6d-f958-4e51-ad33-a197b8e8f43a","resolution":{"observed_at":"2026-08-11T23:18:43.162495Z","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":"2026-08-11T23:18:39.867822Z","title":"From noisy data to feedback controllers: Nonconservative design via a matrix S-lemma,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.867822Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:5bf03797336aa60df0045df8035316cf619d6909be9821c04ed62ecc80c962a9","observation_id":"f3989666-5f7e-4eb9-adad-48d596dfea56","resolution":{"observed_at":"2026-08-11T23:18:39.867822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:18:39.873471Z","title":"Low-complexity learning of linear quadratic regulators from noisy data,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.873471Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:5c24b382c425b26ecf5953de757a2c30935bedca24f0c8411569bcaf95994be9","observation_id":"da6bfd34-2e7a-4f05-8f86-3a308ad4685d","resolution":{"observed_at":"2026-08-11T23:18:39.873471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:18:39.877320Z","title":"On the certainty-equivalence approach to direct data-driven LQR design,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.877320Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:793d7b4c5351cdcf1d5603abcaff2c1e07a52c995595962a3397dfff504d6736","observation_id":"5d40ed42-eced-4a3f-a1b9-162ef94f1122","resolution":{"observed_at":"2026-08-11T23:18:39.877320Z","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":"2026-08-11T23:18:43.122732Z","title":"Robust data- driven state-feedback design,","venue":null,"work_id":"27a0475e-c88b-4d40-88b5-5db2c8d66705","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.881314Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:74044026c45c32a0b29d1fda78db8e23165e9478a3b344567e650c004682a0e5","observation_id":"91eb01c5-54d8-4ccc-b994-d0b575b1fa63","resolution":{"observed_at":"2026-08-11T23:18:43.126896Z","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":"2026-08-11T23:18:43.102040Z","title":"CoRe: Control-oriented regularization for system identification,","venue":null,"work_id":"1995d2cc-87ae-45e6-be9f-7790e700eeed","year":2018},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.885554Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:4d7a4c2158e9158b44d95f693a05bb33f52993e3b12bcc4fac2b56946dd40614","observation_id":"cd5d817b-9ba8-43f1-b0d4-b00cc5cb490c","resolution":{"observed_at":"2026-08-11T23:18:43.112601Z","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":"2026-08-11T23:18:43.050107Z","title":"Structured exploration in the finite horizon linear quadratic dual control problem,","venue":null,"work_id":"b30f92b9-f45e-47d8-b969-3fddb130975d","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.889864Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:4be5ab87a7af1be5aa385db7f6a170b3e41b80200e996a709194787bb0cfa35b","observation_id":"9fe4848b-34e4-4ad1-aa15-7cb067380db1","resolution":{"observed_at":"2026-08-11T23:18:43.073216Z","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":"2026-08-11T23:18:42.989153Z","title":"Bridging direct and indirect data-driven control formulations via regularizations and relaxations,","venue":null,"work_id":"c4be90e9-42cc-4d11-b963-2be4d387a541","year":2022},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.894640Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:6a27b4356c2d53795bfdcb5ca746bd01deca5ba59dbfac09a2e1a31039cbeced","observation_id":"17c36694-445f-4d05-8cad-b258effa9cac","resolution":{"observed_at":"2026-08-11T23:18:43.034584Z","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":"2026-08-11T23:18:42.886035Z","title":"Global convergence of policy gradient methods for the linear quadratic regulator,","venue":null,"work_id":"bf793ee3-8d04-45e5-9620-3a045b9b189e","year":2018},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.898502Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:9a70b2fe7f857d5507c735155b1f1a802ab0585a867664df0d2417e5845b377f","observation_id":"dcb58337-c423-453a-801d-53723a09ea80","resolution":{"observed_at":"2026-08-11T23:18:42.900089Z","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":"1907.08921","last_updated":"2019-07-29T20:26:16Z","snapshot_observed_at":"2026-08-14T16:04:02.994968Z","submitted_at":"2019-07-21T06:36:43Z","title":"LQR through the Lens of First Order Methods: Discrete-time Case","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.08921","snapshot_observed_at":"2026-08-11T23:18:39.983576Z","title":"LQR through the lens of first order methods: Discrete-time case,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:39.983576Z"},"links":{"cited_paper":"/paper/1907.08921","citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:68945e467bd2c052b7b3c8bcfcd4e4fdc6290d08d4cdbaff682bbefee54dc275","observation_id":"0db0a3e1-9728-48ed-a558-380a251be764","resolution":{"observed_at":"2026-08-11T23:18:39.983576Z","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":"2026-08-11T23:18:42.871720Z","title":"Policy optimization for H2 linear control with H∞ robustness guarantee: Implicit regularization and global convergence,","venue":null,"work_id":"7f88dfab-3338-4012-b2dc-db4a1776b20e","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.074884Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:0e7e02ed717924bd3cb0dccb4ea9022beabbfd4942c04d023d8d1f2d35e878d0","observation_id":"4c0d50bf-5b3c-498d-b9bd-7808de83a796","resolution":{"observed_at":"2026-08-11T23:18:42.876871Z","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":"2026-08-11T23:18:40.084260Z","title":"Global convergence of policy gradient methods to (almost) locally optimal policies,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.084260Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:83a43948c910366ff9d9263d726d365e717c7971d8699d36c4bb2ec440262d20","observation_id":"6ce4be40-e172-4246-8517-ba3143c38b5d","resolution":{"observed_at":"2026-08-11T23:18:40.084260Z","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":"2026-08-11T23:18:42.849678Z","title":"Revisiting LQR control from the perspective of receding-horizon policy gradient,","venue":null,"work_id":"49b5421d-ea0d-4834-ac11-0af3c7d831fb","year":2023},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.090287Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:b3a4ee0171c7851fd730870eb4d9dcc8e40b4b30a0c36fb4d34bf395c24848b8","observation_id":"087b28cb-eb24-4081-bcb8-4833df66672d","resolution":{"observed_at":"2026-08-11T23:18:42.854492Z","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":"2026-08-11T23:18:42.826033Z","title":"Toward a theoretical foundation of policy optimization for learning control policies,","venue":null,"work_id":"116ee3bc-c9d1-4da5-93ca-de25ef0dcab7","year":2023},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.106107Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:a50dbb899c418fbf71d91290d8e15ba7d75e6660332a9f1fdce16d21e5e2bd47","observation_id":"823c1de9-5c50-491d-a280-23e8bd78bc73","resolution":{"observed_at":"2026-08-11T23:18:42.839379Z","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":"2026-08-11T23:18:40.117009Z","title":"Adaptive optimal control for continuous-time linear systems based on policy iteration,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.117009Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:9ed340e00f6277e46e03bc1a79a7a131089f99b453982e1d67779358ae6d0c4c","observation_id":"fcc32de5-5ecc-4e7c-b72c-8d831672520b","resolution":{"observed_at":"2026-08-11T23:18:40.117009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:18:40.120823Z","title":"Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.120823Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:4a206af3f1f4b836366d223a64d6078e3e58dbd4ca991122fa26bf3b358b90fd","observation_id":"506f7a90-9c05-4e9d-8aa3-3d0c63e5276d","resolution":{"observed_at":"2026-08-11T23:18:40.120823Z","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":"2026-08-11T23:18:42.797794Z","title":"Value iteration for continuous-time linear time-invariant systems,","venue":null,"work_id":"43d1174c-c860-4a38-8605-b67f132e63a6","year":2022},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.126334Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:766e20176f27cf6cec934f59747f8e907d9ff941cfb3674975c2996b33d16629","observation_id":"3e5d9422-63c6-424e-9f4f-8e2b2415d34e","resolution":{"observed_at":"2026-08-11T23:18:42.802322Z","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":"2026-08-11T23:18:42.591286Z","title":"On-policy data-driven linear quadratic regulator via model reference adaptive reinforcement learning,","venue":null,"work_id":"fd41d5f0-5e99-4745-959f-901b3f02a666","year":2022},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.130450Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:a1ad5290e17ee4554c88c96a7d67ad01c6db9b71285d740bbce18e8ef06ec956","observation_id":"05c6226f-d691-41f3-80d6-b0f023fd4ee7","resolution":{"observed_at":"2026-08-11T23:18:42.716688Z","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":"2402.14483","last_updated":"2024-02-22T12:16:43Z","snapshot_observed_at":"2026-08-17T08:04:36.966347Z","submitted_at":"2024-02-22T12:16:43Z","title":"MR-ARL: Model Reference Adaptive Reinforcement Learning for Robustly Stable On-Policy Data-Driven LQR","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14483","snapshot_observed_at":"2026-08-11T23:18:40.135618Z","title":"MR-ARL: Model reference adaptive reinforcement learning for robustly stable on-policy data-driven LQR,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.135618Z"},"links":{"cited_paper":"/paper/2402.14483","citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:dd6f790ddb87a8540c5d1a9f52934749853b2cc58b3241c36c61b193fb451d76","observation_id":"501dbc93-d9f6-4b4f-be20-a9fb659fb87e","resolution":{"observed_at":"2026-08-11T23:18:40.135618Z","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":"2026-08-11T23:18:42.579975Z","title":"On-policy data-driven linear quadratic regulator via combined policy iteration and recursive least squares,","venue":null,"work_id":"162fd350-a61e-4f61-828f-f38a61593c19","year":2023},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.139509Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:55a7e28d065405d38dafd83581dc924dd30b45d275a9848ea9c83b0359d63084","observation_id":"ec519285-2b34-4e52-b0b2-f5bbf7ba4521","resolution":{"observed_at":"2026-08-11T23:18:42.583796Z","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":"2403.05367","last_updated":"2026-04-10T10:44:15Z","snapshot_observed_at":"2026-08-07T08:49:53.730679Z","submitted_at":"2024-03-08T14:58:34Z","title":"Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient","version":3},"cited_work":{"arxiv_id":"2403.05367","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.05367","snapshot_observed_at":"2026-08-11T23:18:40.779042Z","title":"Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient","venue":"eess.SY","work_id":"8dec6d0a-422f-4ccc-b8fb-ca0799e0e4ed","year":2024},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.143721Z"},"links":{"cited_paper":"/paper/2403.05367","citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:7e379719e9dc2dd401c53b71c747799b0357ee6db64dea54141cde48b8206d04","observation_id":"d6e55226-1b43-4e3f-b7cb-c256d897e066","resolution":{"observed_at":"2026-08-11T23:18:40.785585Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:18:42.521370Z","title":"The role of identification in data-driven policy iteration: A system theoretic study,","venue":null,"work_id":"43189e2d-45fa-4204-bb98-de1ce0350186","year":2024},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.147354Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:e561242a62bb6ca636519f27fcb6219bf8e021f6d910275a8af13843034b8013","observation_id":"cc4178c4-d7e1-46b3-97db-2cb9c9c9e06c","resolution":{"observed_at":"2026-08-11T23:18:42.558283Z","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":"2026-08-11T23:18:40.150264Z","title":"100 years of extremum seeking: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.150264Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:2583798d7db554e3e4a2a735fbb0541aaf44a7ee9c3dd4b497f3fff38b828da8","observation_id":"202eaa08-b51b-4ff2-a5c1-5cdf6d44c0bc","resolution":{"observed_at":"2026-08-11T23:18:40.150264Z","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":"2026-08-11T23:18:42.356807Z","title":"Adaptive extremal control,","venue":null,"work_id":"f84bc538-d75e-4849-b09a-db9ab5379bcf","year":1995},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.154208Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:203ef642e3ad012585ba594c79f343d023d5d23c5cb0d0c9c9c25d067442a4b9","observation_id":"3d08894a-77e1-4430-a61d-5bd4b03b0339","resolution":{"observed_at":"2026-08-11T23:18:42.410508Z","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":"2026-08-11T23:18:42.344835Z","title":"Solving smooth and nonsmooth multivariable extremum seeking problems by the methods of nonlinear programming,","venue":null,"work_id":"32ca711a-ebe4-44ba-8f85-6d31ceb4ac1c","year":2001},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.187454Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:7a06989d245ce0e8dd32bd4db824ec05f92c5019f12b8545940eaf8fb1c2b67f","observation_id":"c390302e-7632-4574-a1e7-6388edfe80f0","resolution":{"observed_at":"2026-08-11T23:18:42.348942Z","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":"2026-08-11T23:18:42.171635Z","title":"Ariyur and M","venue":null,"work_id":"a13cf645-0d2e-4c52-8bbd-747d20fdeeca","year":2003},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.269683Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:e5f4941e3abcae3ea725c5dad599f15d3aa09ee01adcb46f02b75f19a3916ebd","observation_id":"c2746736-47dc-4a84-a196-43909ff1a6ce","resolution":{"observed_at":"2026-08-11T23:18:42.291508Z","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":"2026-08-11T23:18:40.353333Z","title":"Stability of extremum seeking feedback for general nonlinear dynamic systems,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.353333Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:28d5028b69119fd2a77d381b3c044c7749e6123a6da5db9aa19db97a77b7e919","observation_id":"621b8f0b-bfb1-44dd-99dd-4d12d893b46d","resolution":{"observed_at":"2026-08-11T23:18:40.353333Z","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":"2026-08-11T23:18:42.149532Z","title":"On non-local stability properties of extremum seeking control,","venue":null,"work_id":"32b04f43-37a3-4670-b805-3838af036141","year":2006},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.401914Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:b2bdc50ddc91193d635fdc3a4b2760d6b77308b383d8263095ae5b7abae58b2e","observation_id":"77293a28-0255-40fe-8332-526af4e6d346","resolution":{"observed_at":"2026-08-11T23:18:42.154952Z","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":"2026-08-11T23:18:42.032992Z","title":"Finite-horizon LQ control for unknown discrete-time linear systems via extremum seeking,","venue":null,"work_id":"9f69efb7-bb6c-4dd1-ab59-97650c4df9b7","year":2013},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.410431Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:65a7af4da86fba179d383bf6213055fed5d4b3435536595858d550897f081215","observation_id":"06f91ae3-73a3-4781-8f46-291b7a422e04","resolution":{"observed_at":"2026-08-11T23:18:42.068463Z","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":"2026-08-11T23:18:40.417223Z","title":"Nonlinear systems,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.417223Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:d920f0ab2d96b190510900611fa0c7dcf040577ec625d7c2fbe1dddee30fcd6f","observation_id":"b728b295-cf73-43f2-97d0-392d01fc37ac","resolution":{"observed_at":"2026-08-11T23:18:40.417223Z","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":"2026-08-11T23:18:41.992896Z","title":null,"venue":null,"work_id":"5813779a-cac0-476e-a23a-8ca469996eba","year":2007},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.425002Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:79e8af55eaac742f18ac305b51a24d5083249baee114224e39fe2a0d2165d9c5","observation_id":"44b06b14-8793-4802-9309-624d00c543a4","resolution":{"observed_at":"2026-08-11T23:18:42.015881Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-11T23:18:41.944950Z","title":"Averaging analysis for discrete time and sampled data adaptive systems,","venue":null,"work_id":"c3101d1d-1588-4708-b449-212f421e9823","year":1988},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.443765Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:ad307cb2eff804641a43dfadb9b87694aba71ac9fc63880e0a9d09c875fa5b48","observation_id":"1c76df38-2db0-4c55-a905-2397dd795091","resolution":{"observed_at":"2026-08-11T23:18:41.949546Z","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":"2026-08-11T23:18:41.831138Z","title":"Extremum-seeking policy iteration for data-driven LQR,","venue":null,"work_id":"cf012b30-e016-49ff-aa91-bdb5f5b0b9f0","year":2024},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.453812Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:eb9ac91fb4ed51cb4da0606e6dc5ffd05ac2d57f310ab4c98cc6d538b942feae","observation_id":"11a6a7cf-6952-49aa-a9b3-100ea7a6360a","resolution":{"observed_at":"2026-08-11T23:18:41.922771Z","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":"2026-08-11T23:18:40.457950Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.457950Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:5bc9c99e4847b72970664d5171a19db43e7df5b5bf5e8095fbd435b4c9e9686c","observation_id":"9af8c156-26bc-47f3-9fac-4e15307ae1c9","resolution":{"observed_at":"2026-08-11T23:18:40.457950Z","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":"2026-08-11T23:18:41.808657Z","title":"On topological properties of the set of stabilizing feedback gains,","venue":null,"work_id":"5891f29c-0cb4-4c08-a35c-989e0427d045","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.461988Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:b2ec8d57e944f215175f48aa5ae30e9898dd6ccffb3d98cdfd25336b9c828b75","observation_id":"0ee85323-3e18-492a-8298-0efc08fc6e1b","resolution":{"observed_at":"2026-08-11T23:18:41.814616Z","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":"2026-08-11T23:18:41.721794Z","title":"Personalized optimization with user’s feedback,","venue":null,"work_id":"602005b7-3075-45a7-be96-a693132bc37e","year":2021},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.466408Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:5156ebb2d65290cf9c5de5b7ae0e5906fe183a11f295dbfb36cf41f0cacd0d8a","observation_id":"b64beaf6-5d8d-40e9-8829-5fa41b8e893e","resolution":{"observed_at":"2026-08-11T23:18:41.765200Z","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":"2026-08-11T23:18:41.539801Z","title":"LQR via first order flows,","venue":null,"work_id":"988d34b3-163e-4d85-9c3b-9adfa11395ec","year":2020},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.470696Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:8d4b4c056bf9be6b7020c619f6abf48645b71a7374ade37c9c47ec9269780093","observation_id":"f55833bb-11f8-4f66-b087-99f6fbcc4b44","resolution":{"observed_at":"2026-08-11T23:18:41.685647Z","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":"2026-08-11T23:18:41.234859Z","title":"Leonhard, Control of electrical drives","venue":null,"work_id":"0da0050e-c604-41b1-b7fd-2c5cbd8a9254","year":2001},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.474444Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:3ce2eb304c9cefbb83db3b7b50294924ea6b62ba84453d7321e1e1364684c27b","observation_id":"c7e10e4f-871c-459e-b504-746e3b208c10","resolution":{"observed_at":"2026-08-11T23:18:41.385080Z","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":"2026-08-11T23:18:41.032728Z","title":"Sines and cosines of angles in arithmetic progression,","venue":null,"work_id":"44a15af6-7a5c-4c26-9a32-81366afa19b9","year":2009},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.478145Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:45283a7ebfca2e2e7ad3362a8098b9fdaca621a8c875fa5320ef7b893951fc25","observation_id":"2846d27a-d840-4051-8c31-1ae8452181e4","resolution":{"observed_at":"2026-08-11T23:18:41.102800Z","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":"2026-08-11T23:18:41.017170Z","title":"On the discrete analogy of Gronwall lemma,","venue":null,"work_id":"41a9e3a1-01c5-4704-93cd-d660000c420d","year":1983},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.481431Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:f473646d0a3ac7eb8e6b916019461f870863fb457acc51a78bc4505441e782ef","observation_id":"ff8312b6-5150-48a5-a133-10bff0a619e3","resolution":{"observed_at":"2026-08-11T23:18:41.022405Z","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":"2026-08-11T23:18:40.903955Z","title":"Discrete Gronwall lemma and applications,","venue":null,"work_id":"4b1e4287-ff05-4cef-817e-05d042160dd0","year":2009},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.585571Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:70509a8e8e8bec4bef91d984d61ab4137134676271dea0913e9c61c7d9cd385c","observation_id":"832cfc43-1dae-4cc2-a6dd-b90a28cefd99","resolution":{"observed_at":"2026-08-11T23:18:40.984521Z","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":"2026-08-11T23:18:40.881625Z","title":"Extremum seeking tracking for derivative-free distributed optimization,","venue":null,"work_id":"716d5650-59fe-4ff2-9ed6-8f86c96a09bb","year":2024},"citing_paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T23:18:40.669871Z"},"links":{"citing_paper":"/paper/2412.02758"},"observation_digest":"sha256:03474d8343b904b6bd3c0a0cfc13de6f22e2126c26137c375f6e2a2bac5f0c15","observation_id":"4326bb4a-ffba-434e-b24b-eace5a56191a","resolution":{"observed_at":"2026-08-11T23:18:40.892933Z","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"}}],"paper":{"arxiv_id":"2412.02758","last_updated":"2025-06-12T16:42:29Z","latest_version":3,"primary_category":"math.OC","snapshot_observed_at":"2026-08-14T11:51:40.132601Z","submitted_at":"2024-12-03T19:00:29Z","title":"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":41},"total_outbound_references":56},"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 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2412.02758."}