{"as_of":"2026-08-12T21:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6d8a61a42449e461f900c96f87fe6074d72b53eeecebb54b254ffffcbe1e3582","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T15:24:30.497012Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":5,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-12T15:24:30.497012Z","title":"Lixing Song, Junheng Wang, and Junhong Xu","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.14321","last_updated":"2025-06-03T11:15:35Z","snapshot_observed_at":"2026-08-12T20:30:27.833309Z","submitted_at":"2024-11-21T17:10:36Z","title":"Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T15:24:30.497012Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2411.14321"},"observation_digest":"sha256:4c93e6f57ee9c08f74d91061fc5619f75beaed23f7583e0229d54e880cd0b3e0","observation_id":"3d0460ec-4019-4c64-a1a3-dd0c4dc1201b","resolution":{"observed_at":"2026-08-12T15:24:30.497012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":"2408.04200","doi":"10.48550/arxiv.2408.04200","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Koopman operators in robot learning","venue":"arXiv (Cornell University)","work_id":"ce94c90e-32e8-4a01-b106-530e9aa3ce3e","year":2024},"citing_paper":{"arxiv_id":"2503.21491","last_updated":"2025-03-27T13:27:46Z","snapshot_observed_at":"2026-08-11T14:46:53.836648Z","submitted_at":"2025-03-27T13:27:46Z","title":"Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-22T22:37:07.608847Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2503.21491"},"observation_digest":"sha256:e9075a41a63a22421f3596db03e94cf4b81f4a19f1e386162c4dc3232137bf2e","observation_id":"bae931a2-3f97-441f-85f7-62c164186fea","resolution":{"observed_at":"2026-05-22T22:37:12.482938Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":"2408.04200","doi":"10.48550/arxiv.2408.04200","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Koopman operators in robot learning","venue":"arXiv (Cornell University)","work_id":"ce94c90e-32e8-4a01-b106-530e9aa3ce3e","year":2024},"citing_paper":{"arxiv_id":"2506.14083","last_updated":"2026-04-07T05:01:52Z","snapshot_observed_at":"2026-08-04T01:13:03.586415Z","submitted_at":"2025-06-17T00:48:25Z","title":"Extracting transient Koopman modes from short-term weather simulations with sparsity-promoting dynamic mode decomposition","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-19T10:07:17.688238Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2506.14083"},"observation_digest":"sha256:05f36d301ab6afd8e20dfeb742cd65b92a8bde5e1a19717146a0a14867a13b8f","observation_id":"095ceb6a-d8c3-4544-b057-6a937379a6b3","resolution":{"observed_at":"2026-05-19T10:12:14.619481Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-03T11:32:56.537411Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06315","last_updated":"2026-06-22T11:12:51Z","snapshot_observed_at":"2026-08-10T20:24:51.196745Z","submitted_at":"2026-01-09T21:18:01Z","title":"Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T11:32:56.537411Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2601.06315"},"observation_digest":"sha256:04c20f897a0c96781ede6d0dc1b3f5b30a685ea90f40f69a767e43d71379d1ae","observation_id":"64e63271-d7f9-4331-85b9-11cf89e701e7","resolution":{"observed_at":"2026-08-03T11:32:56.537411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":"2408.04200","doi":"10.48550/arxiv.2408.04200","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Koopman operators in robot learning","venue":"arXiv (Cornell University)","work_id":"ce94c90e-32e8-4a01-b106-530e9aa3ce3e","year":2024},"citing_paper":{"arxiv_id":"2606.17070","last_updated":"2026-06-06T03:53:39Z","snapshot_observed_at":"2026-08-03T05:27:46.222661Z","submitted_at":"2026-06-06T03:53:39Z","title":"KFTD: Koopman-Fourier Time-Differentiable Network for Continuous Ocean Spatiotemporal Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-27T19:11:15.490486Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2606.17070"},"observation_digest":"sha256:b7267744c349a87729c05a219dc3fc1496c04f1e033f6d346211c6a6023cb02e","observation_id":"1dea09d4-ffd1-4a6c-9eea-db079ae6b392","resolution":{"observed_at":"2026-07-02T22:07:26.429089Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":"2408.04200","doi":"10.48550/arxiv.2408.04200","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Koopman operators in robot learning","venue":"arXiv (Cornell University)","work_id":"ce94c90e-32e8-4a01-b106-530e9aa3ce3e","year":2024},"citing_paper":{"arxiv_id":"2606.23957","last_updated":"2026-06-22T21:36:55Z","snapshot_observed_at":"2026-08-07T10:58:01.395394Z","submitted_at":"2026-06-22T21:36:55Z","title":"Learning the Koopman Operator using Attention Free Transformers","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-06-26T08:49:32.561520Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2606.23957"},"observation_digest":"sha256:90ebcad19345d36b0c695c342d82be2827a3e11ce9d04062edf85933ec19ecd9","observation_id":"edaaf2c4-7f5a-4b77-b413-04f1e0498576","resolution":{"observed_at":"2026-06-26T08:59:15.437470Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-01T00:11:34.262197Z","title":"Koopman operators in robot learning.arXiv preprint arXiv:2408.04200, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.26345","last_updated":"2026-07-28T23:37:55Z","snapshot_observed_at":"2026-08-10T21:05:09.894841Z","submitted_at":"2026-07-28T23:37:55Z","title":"MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T00:11:34.262197Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2607.26345"},"observation_digest":"sha256:9b6dbb8949dc26ccfab218ea5e47a00368b8586c5778d6ac469fc5839b4b2431","observation_id":"d13860ce-f2aa-4ce7-ae05-ed9377244438","resolution":{"observed_at":"2026-08-01T00:11:34.262197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04200","snapshot_observed_at":"2026-08-01T17:24:15.057398Z","title":"arXiv preprint arXiv:2408.04200 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26370","last_updated":"2026-07-29T01:07:28Z","snapshot_observed_at":"2026-08-07T06:07:13.986442Z","submitted_at":"2026-07-29T01:07:28Z","title":"Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-01T17:24:15.057398Z"},"links":{"cited_paper":"/paper/2408.04200","citing_paper":"/paper/2607.26370"},"observation_digest":"sha256:c932678201a8e6d3ff9e5d848b226dfc7c93fe6421c51c5acd329aab9afef991","observation_id":"42b6694b-dabc-410c-b2bc-7e35dbbc8d0a","resolution":{"observed_at":"2026-08-01T17:24:15.057398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.04200/citation-record","integrity":"/paper/2408.04200/integrity","json":"/paper/2408.04200/citation-record.json","paper":"/paper/2408.04200"},"outbound":[],"paper":{"arxiv_id":"2408.04200","last_updated":"2025-05-23T08:04:37Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-10T20:24:13.436011Z","submitted_at":"2024-08-08T03:54:48Z","title":"Koopman Operators in Robot Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.04200."}