{"as_of":"2026-08-13T10:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eaa8635f3db998092d92a266a71ad266a7c28b3f79b266801afe9d9eac22e6b3","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T21:05:06.139774Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.08241/citation-record","integrity":"/paper/2509.08241/integrity","json":"/paper/2509.08241/citation-record.json","paper":"/paper/2509.08241"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:04.171808Z","title":"Kleff, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.171808Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:75847b923e5ee0f7f586e426e6153006c7c4a9197bee2598df06d9ea6ba29362","observation_id":"8a347263-995f-40f0-911d-650a812434c3","resolution":{"observed_at":"2026-08-04T21:05:04.171808Z","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":"2026-08-04T21:05:04.189937Z","title":"Grandia, F","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.189937Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:41e286df3c162c09c402209fb0f2efdf933ac875e4cf34e6cad5c0445a294195","observation_id":"515fbfed-d9c0-45bd-81bb-1c437a1f33c5","resolution":{"observed_at":"2026-08-04T21:05:04.189937Z","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":"2022.32283","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:08.144057Z","title":"Meduri, P","venue":null,"work_id":"681cc30a-ce95-4cc8-a2a0-d7348ecc16af","year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.212528Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:9ab254940e2e2a3ccffd1036ee13db7ca737976edeb19a8c362674fc0bf0b2cf","observation_id":"de8cd6ae-a19b-403a-9d2b-bb4a70c4b296","resolution":{"observed_at":"2026-08-04T21:05:08.190979Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.33515","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.926948Z","title":"Le Cleac’h, T","venue":null,"work_id":"53b5cf9c-0554-406a-b65f-2c08ac4f8216","year":2024},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.226196Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:5c67082c10ecd807fc64efa85ab7a4a630cb0785ad84f8bb9de38c0ad82b8478","observation_id":"2755e7bb-25ca-4709-9c54-583e84663b17","resolution":{"observed_at":"2026-08-04T21:05:07.992606Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-04T21:05:04.237137Z","title":"Schulman, F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.237137Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:e640e79e2c838ae576c3691add5d8961333325cde7249e9e9c5f90047c0e04c2","observation_id":"86d5dab0-ef1a-4a30-8671-f61e2acf4b13","resolution":{"observed_at":"2026-08-04T21:05:04.237137Z","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":"2026-08-04T21:05:11.008854Z","title":"Fujimoto, H","venue":null,"work_id":"23cb8364-8fb7-42a5-a54a-fa940394ee6e","year":2018},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.247783Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:47425f516931ad49ffc81387ffdf92be710a0e76f05a786544a439ae587254b3","observation_id":"55459a89-72c0-4e4f-81af-ef0d3e336363","resolution":{"observed_at":"2026-08-04T21:05:11.023639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:10.885790Z","title":"Haarnoja, A","venue":null,"work_id":"c6b75f4f-02e6-4898-b36e-75b4cce65de4","year":2018},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.256052Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:51456911e7238f7a52ed44e519b00aa5a951725abb27fc35f22983af4f19269d","observation_id":"e95d90e8-ad8b-4266-8ef8-39defe5eb9f5","resolution":{"observed_at":"2026-08-04T21:05:10.938778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:10.746521Z","title":"Jitosho, T","venue":null,"work_id":"a4ea56fd-1597-4707-87b5-e7a9e3386a91","year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.264381Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:a45b5448ee70c2294a7e545436fa7a11748139df495eafd186cd93aabfaa6719","observation_id":"6f5ee181-6d62-4090-b559-5d284e156cc6","resolution":{"observed_at":"2026-08-04T21:05:10.799647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.273283Z","title":"Rajeswaran, V","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.273283Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:ea1582e3d9304cb545d8dff51f6718ae4bb33020b1ebe94ab9be891f6b954f0f","observation_id":"504a485c-1bb9-4146-bbfc-2f063b09fa70","resolution":{"observed_at":"2026-08-04T21:05:04.273283Z","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":"2026-08-04T21:05:10.664441Z","title":null,"venue":null,"work_id":"a31fb0ff-382e-41d7-9583-ca31b2114f8b","year":2022},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.284199Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:28e9d4f269064db2f70098037cf0db38f88458f710a753712927a233df1137c2","observation_id":"85591910-9621-449c-b7fe-a50fe0f04d9b","resolution":{"observed_at":"2026-08-04T21:05:10.718072Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.294781Z","title":"Williams, N","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.294781Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:8844ea64fc46eef8838cf94549ecd4646f04d25b0f1f4faa2a9ba7b610a38553","observation_id":"c139b2a4-f876-4f0b-8938-f9df54b40469","resolution":{"observed_at":"2026-08-04T21:05:04.294781Z","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":"2026-08-04T21:05:04.305318Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.305318Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:098153a9011cd22d986985a47feedc4e106b71c7ff6bd3d1bdc08ae18c4c0bac","observation_id":"9be20d2d-ec62-4c61-8098-dd4bdaf8b909","resolution":{"observed_at":"2026-08-04T21:05:04.305318Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.15100","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.780936Z","title":null,"venue":null,"work_id":"66fb6e11-fd61-493f-94e2-6f35241ff156","year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.315346Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:244b92e09265ce5aab35d2af23359d882d49ed138816a34a0f2bbef3d3671169","observation_id":"818ccfd4-b742-4473-b724-360ce215188b","resolution":{"observed_at":"2026-08-04T21:05:07.835678Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:10.512035Z","title":null,"venue":null,"work_id":"7158fd69-0675-40bc-a7f3-0e14517b22ee","year":2013},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.332320Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:b6e90cd107523046755c21f78fc0db68734d13019a21eec3a7b934ffa7706bed","observation_id":"e2ef6318-09f5-4354-8d9f-38c3080485ac","resolution":{"observed_at":"2026-08-04T21:05:10.580579Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.342337Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.342337Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:feda3556f50b4aab174f876beb30ea8ba257e1993218b8b2b745f48bacece794","observation_id":"e02fd41f-0fc1-4ac7-ab3d-9edcb8b877b9","resolution":{"observed_at":"2026-08-04T21:05:04.342337Z","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":"2026-08-04T21:05:10.433817Z","title":null,"venue":null,"work_id":"a85b1659-ef9f-4d18-afa0-ce2c2242a50d","year":2017},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.349730Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:b38903b1e6703adfec87ea714f3fec443929bf9de965781df486a048b08703fc","observation_id":"f350b2b8-26ad-41b4-9b6b-8a30d796319c","resolution":{"observed_at":"2026-08-04T21:05:10.471810Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.355894Z","title":"Geneva and N","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.355894Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:7425b4d75111e39762465481a5374b164a37d1aad90bcd6d9fa37508720ab04f","observation_id":"10c81c8a-efb4-412d-a3b2-3c19bc36f66e","resolution":{"observed_at":"2026-08-04T21:05:04.355894Z","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":"2010.55893","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.657789Z","title":"Susuki and I","venue":null,"work_id":"eb741371-1ec7-48df-8a86-c3c2e49adfc3","year":2010},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.366040Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:0714954739398b92d155b56ef918341996937d1c6737f3158c98d1442bd13464","observation_id":"61909c8f-4c13-41cd-97e6-477511d63ed2","resolution":{"observed_at":"2026-08-04T21:05:07.715756Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.376748Z","title":"Bruder, B","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.376748Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:b406be8e35924a0a1db6b0c457f82f29821ba622c4e9cdbd1c0d1a0468e3b19c","observation_id":"3230d915-bd3f-4d31-84e4-d739acfaaa53","resolution":{"observed_at":"2026-08-04T21:05:04.376748Z","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":"10.1177/02783649241272114","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Bruder, D","venue":"The International Journal of Robotics Research","work_id":"e7dd3d11-f346-4a33-bc69-8930ad445cdd","year":2024},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.418409Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:2e31ba7f60b7ece2abf3aa13f5cc1a6af43e3c28c8f5857b05dbc6fbc9c7eb2c","observation_id":"987a8179-630d-4fb7-9bd1-fbc0d878378a","resolution":{"observed_at":"2026-08-04T21:05:06.653561Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29238","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.537895Z","title":"Abraham and T","venue":null,"work_id":"cc077bfd-0a67-4b2b-a592-5b7e3530855e","year":2019},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.489289Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:5e465eec70aa5fdd8154aa794555bec8085867a2f0bc3bee4ec0c8cdfee415e5","observation_id":"c7a36f35-bac9-470f-a78c-0b4554c3574e","resolution":{"observed_at":"2026-08-04T21:05:07.600632Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.30765","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.402922Z","title":"Mamakoukas, M","venue":null,"work_id":"11faf57c-4050-413c-8a6a-82e219bfd27b","year":2021},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.507898Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:2591d186a90ff823a5acb605088e211dc8ee78f0fa132b79d94f41a14af0cbad","observation_id":"0eebd01a-ce9b-44a2-9571-2f871d049f41","resolution":{"observed_at":"2026-08-04T21:05:07.501678Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:10.329389Z","title":null,"venue":null,"work_id":"32d2aede-5500-40cf-8b12-e617d59db7b7","year":2025},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.540346Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:2305c266975c1e052a5e448ea1a00774bcc790a71f45517fddc030a3eedcbfe7","observation_id":"91d5acf1-0272-4034-9c9a-7dbfb3164f1e","resolution":{"observed_at":"2026-08-04T21:05:10.369792Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:10.194531Z","title":null,"venue":null,"work_id":"6849b6e6-1d8f-4413-8f9a-9960cd1951ae","year":null},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.577408Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:64d4e4c154e7d529c1f902ae84536d6608872f51d8df5e37e06a0ca2c38f2caa","observation_id":"044a9c0a-19ca-42c2-ad0d-0576e35a2c54","resolution":{"observed_at":"2026-08-04T21:05:10.282622Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.727583Z","title":null,"venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.727583Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:bcf3a8d73ce4d93015226b19e9c3ee32fbd4a8e2946fa7ae189298b9256ca3cb","observation_id":"dcd148f4-df8c-4035-a27c-8d30a2210724","resolution":{"observed_at":"2026-08-04T21:05:04.727583Z","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":"2026-08-04T21:05:09.951508Z","title":"Nagabandi, K","venue":null,"work_id":"1c6293bb-800f-4a18-9a6a-a2ce03b21aa5","year":null},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.798756Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:f1935bf0923e590029d3f5cbe72fc4c99fec017232c40a1e5ac612153989272d","observation_id":"68e15270-7760-4b29-afa8-f54f92b4e8c6","resolution":{"observed_at":"2026-08-04T21:05:10.008961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/18m1192329","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, C","venue":"SIAM Journal on Applied Dynamical Systems","work_id":"4d878824-25be-4171-9363-78b05fa3888e","year":2019},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.873250Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:c809401b4b2ef00e7be5a1dac1377961861cf7dd9c824f32c97b58e992419de3","observation_id":"494f5936-34ab-44cb-974a-e29b3035ecf3","resolution":{"observed_at":"2026-08-04T21:05:06.529917Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:04.910368Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.910368Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:01b3710b0dcc1b3b242c5f65302c25e78024191e9667b425cfd6cf46ea6eb9cd","observation_id":"5b401602-3256-459d-9761-b03b8f8876cd","resolution":{"observed_at":"2026-08-04T21:05:04.910368Z","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":"2026-08-04T21:05:04.996420Z","title":"N ¨uske, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.996420Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:2cd40f936edd23746b4117374474df172cea7397507caa24eb65a5f917275d84","observation_id":"b10bf17b-7379-452e-bd9e-b3c9ff4ce308","resolution":{"observed_at":"2026-08-04T21:05:04.996420Z","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":"2026-08-04T21:05:09.758286Z","title":"Zhang and E","venue":null,"work_id":"7327324f-589d-4390-94b1-5549d8b465a3","year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.052964Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:7d03eb117da96d562ef1a93b6358473885ea7fa962c11d8579540404d5379eed","observation_id":"1fe6c94c-4ce9-4956-8bc8-0be37e09d567","resolution":{"observed_at":"2026-08-04T21:05:09.783965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02494","last_updated":"2024-05-23T09:04:32Z","snapshot_observed_at":"2026-08-13T04:27:22.433520Z","submitted_at":"2024-02-04T13:58:48Z","title":"Variance representations and convergence rates for data-driven approximations of Koopman operators","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02494","snapshot_observed_at":"2026-08-04T21:05:05.127093Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.127093Z"},"links":{"cited_paper":"/paper/2402.02494","citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:05d9cb91d603c20506a5fba26e0dff4583fec67573a897786869dd359c64a9ac","observation_id":"a9d4ef65-5aed-4eaf-a5bb-3c13ba5f6860","resolution":{"observed_at":"2026-08-04T21:05:05.127093Z","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":"2026-08-04T21:05:09.660695Z","title":null,"venue":null,"work_id":"ca91b40e-5301-4a44-b61e-fcb0faf3a05e","year":1960},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.179258Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:c9a427f28a51176a07fd4581dca891a50d1db63b4455e4762befbf01f6a88b88","observation_id":"34920a5c-2947-4122-a5f4-c45d050657a0","resolution":{"observed_at":"2026-08-04T21:05:09.698041Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:05.234896Z","title":null,"venue":null,"work_id":null,"year":1931},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.234896Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:169f3f6ee45b2396bf6b56dde0cb41a8b24ab30b7dbc71ea89571973f7e08e29","observation_id":"b39feebb-cc6e-4030-98ca-23dd956771d5","resolution":{"observed_at":"2026-08-04T21:05:05.234896Z","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":"2023.32535","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.272207Z","title":null,"venue":null,"work_id":"d86ad04f-3221-4f9b-953f-f6205af7ddd5","year":2023},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.314436Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:97c6093cbc5993540923a864dd3ed2ea2d15bd9d26f354097aa309caccbe2a8a","observation_id":"70df344f-486e-4f66-8f3d-7c070c7095e5","resolution":{"observed_at":"2026-08-04T21:05:07.335566Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:05.354839Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.354839Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:2daf22a747dfb14bf13727a11a28434fd1f1abad347ec269a93e5b13f614609f","observation_id":"8c90d553-d0c3-4c2f-9678-50f69d81ec38","resolution":{"observed_at":"2026-08-04T21:05:05.354839Z","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":"stable/2236561","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:07.119773Z","title":"Sherman and W","venue":null,"work_id":"6dfcdec5-705a-43ac-a276-79318244df1e","year":1950},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.392479Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:a99354ccadbe120e76c757d3b6ed4136e141870ca56e4871daef324fb53a95b7","observation_id":"749bd4fe-34d2-4c31-be52-0f442118a21b","resolution":{"observed_at":"2026-08-04T21:05:07.186634Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.25967","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:06.987301Z","title":null,"venue":null,"work_id":"4bea7636-bf27-468a-8b86-7942156305bf","year":2016},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.459326Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:aaedf58ef007d9976cee24abd7f99b837294e78ab7b02ca2617e68778df8ede0","observation_id":"58d7a302-1905-4911-b63c-2257fbf863e5","resolution":{"observed_at":"2026-08-04T21:05:07.052656Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1177/02783649211037697","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nishimura and M","venue":"The International Journal of Robotics Research","work_id":"150624e3-e919-4dfe-b9ff-fc310304e494","year":2021},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.518653Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:40de17aad1d65e1eb04681a7cbb28d07281f8c3266d1d849b00532685338da95","observation_id":"deea914f-54cb-407c-9cda-2128190ffe28","resolution":{"observed_at":"2026-08-04T21:05:06.415320Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:09.545682Z","title":"Ketchum, J","venue":null,"work_id":"dfeec9b7-68a4-43e7-86d7-af63cd1e878b","year":2025},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.595151Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:0e35af42f7d982a7662cce33e53da1ef01e826491ef66ffaaa87f0ba0c43a854","observation_id":"21f1f06e-c1f0-4f62-ab22-845352fd0f57","resolution":{"observed_at":"2026-08-04T21:05:09.614995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:05.607354Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.607354Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:0afe5b36498c226c51935d0411e71ef997596e9b7657522c13a33f0deb216e8e","observation_id":"f0644007-7678-4864-8845-b5a5e58711e2","resolution":{"observed_at":"2026-08-04T21:05:05.607354Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.15607/rss.2017.xiii.052","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Abraham, G","venue":null,"work_id":"51960487-2f25-4fb5-91ee-d2a0b37f5d4d","year":2017},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.660725Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:37ee85f1f04ff1b04dd88247c4d23c23e9b8bf3e046f7b7a656a030b14b9c67b","observation_id":"e405f2ff-c653-49c6-a77b-67c9978c7444","resolution":{"observed_at":"2026-08-04T21:05:06.262459Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:09.445018Z","title":null,"venue":null,"work_id":"099307ff-46b2-4872-9c5a-c9112b34b703","year":null},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.699189Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:4ddd3e8e65443ec662d67426d01c5b0c12f5b2a7e33f4699833531de500d05a8","observation_id":"c1416384-f9a9-452d-9964-1da9a1c2389f","resolution":{"observed_at":"2026-08-04T21:05:09.486751Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:09.213330Z","title":"Avtges, J","venue":null,"work_id":"7cb8b335-c16c-4ccc-aace-d58d4007baae","year":2025},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.838867Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:746cc820ef298f25882b26d783a664a85f6a0636aaef9249919677f833913dd5","observation_id":"a9533242-ddc1-4a2a-bbb0-5d717f534351","resolution":{"observed_at":"2026-08-04T21:05:09.252378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:09.070730Z","title":"Boyd and L","venue":null,"work_id":"f2883199-32e5-4677-97ea-2afc0f0e58d5","year":2018},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.869737Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:ee67d42654b21f2652293c2c39dc58f23ac8267534f55f6f8813a78c0b665b94","observation_id":"12202301-4789-4e78-b7a3-ed4f3a753e7b","resolution":{"observed_at":"2026-08-04T21:05:09.129772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:08.924507Z","title":null,"venue":null,"work_id":"fca85b83-da8a-44c1-bc4c-9605564c9430","year":1969},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.895336Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:f58d331a2e9f64476a0a5827dd395ad1c252c223fd0b4889560444ba32584b84","observation_id":"e952bee5-1261-4ba6-90e6-02fdfc3f514f","resolution":{"observed_at":"2026-08-04T21:05:08.980629Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:08.770106Z","title":"Foundation","venue":null,"work_id":"30875bab-cf83-4d0d-9f07-8f6ded6e8e0c","year":null},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.961038Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:6f8bc40b3b24652b875cc817434d95dfa4ef71db1a22e2101a12a0e895503221","observation_id":"d4614651-6b73-471c-83c4-65f76b1cf320","resolution":{"observed_at":"2026-08-04T21:05:08.826416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:08.518577Z","title":null,"venue":null,"work_id":"9cc11fa8-2200-4d4f-9451-db272cacd652","year":2024},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:06.021893Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:06270ec658b91e0c34a3bbae1be4343057bf58d99b0bda42b0b9031401759e15","observation_id":"b4a13b58-fd35-42d4-9519-c946dd7c050b","resolution":{"observed_at":"2026-08-04T21:05:08.667585Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:08.313706Z","title":"Raffin, A","venue":null,"work_id":"960fda06-a46d-403c-ae8b-31f28820e77c","year":2021},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:06.076252Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:48c6692643c23e08e7970e2fd4924eeeb18ed74dc12cf522c88fcad5801d4137","observation_id":"e035135c-4bb4-4578-ab81-ca376ffa129f","resolution":{"observed_at":"2026-08-04T21:05:08.412644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.74872","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:05:06.776486Z","title":"Williams, P","venue":null,"work_id":"2bed1223-24ab-4e1b-be91-3ee73b01e48d","year":2016},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:06.139774Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:7482111670e77c471e9948eec4b9b43e068bace8ed3f26063884b7ba3e7419bc","observation_id":"a877439f-12f0-4487-82b0-a6c3bf563907","resolution":{"observed_at":"2026-08-04T21:05:06.873831Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:09.834410Z","title":"URLhttps://proceedings.mlr.press/v100/ nagabandi20a.html","venue":null,"work_id":"a6c619a5-9820-43b6-b608-fae98fb05f29","year":2020},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":1112,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.835881Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:0d2c59fd76d59dab3129e64e6b04c1db5a09132eaafae78d7469c85333c89179","observation_id":"0d277d62-12bc-47e8-b3c2-4fb071431a10","resolution":{"observed_at":"2026-08-04T21:05:09.872022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:10.082660Z","title":null,"venue":null,"work_id":"85bb9181-c938-4382-9fc4-25d6b1b2426d","year":1988},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":1988,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:04.647365Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:a7b185205ed2f08553fd1f17f32a1bd3bed33202084a56f112cb517f3435cf76","observation_id":"6e4e0e16-2209-49c6-9784-d4de61ff3a45","resolution":{"observed_at":"2026-08-04T21:05:10.113984Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-04T21:05:09.341475Z","title":null,"venue":null,"work_id":"8fd1b6cf-4b8e-4205-b266-c7585e67cb3d","year":null},"citing_paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-04T21:05:05.770512Z"},"links":{"citing_paper":"/paper/2509.08241"},"observation_digest":"sha256:2b6387be11083309846e19ba7c3f907a300f4d1501e53228be18cb73bae49ffd","observation_id":"fd2484ff-d542-4301-bbf9-83f18ba2ab3a","resolution":{"observed_at":"2026-08-04T21:05:09.382596Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.08241","last_updated":"2025-09-10T02:47:42Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-04T21:05:01.512090Z","submitted_at":"2025-09-10T02:47:42Z","title":"Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":5,"metadata_mismatch":7,"parse_uncertain":0,"unresolved":24,"verified_exact":5,"verified_fuzzy":11},"total_outbound_references":52},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2509.08241."}