{"as_of":"2026-08-17T09:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1774cf8b46e484999c24b81564e466cd664309d42a7955ed397788b730acc192","coverage":[{"denominator":7,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:19:37.266964Z","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":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T19:11:49.407111Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T23:20:53.676982Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"cited_work":{"arxiv_id":"2507.14354","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.14354","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Interpretable gradient descent for kalman gain","venue":null,"work_id":"443ec87f-1232-4c06-b180-39def7769211","year":2025},"citing_paper":{"arxiv_id":"2604.05071","last_updated":"2026-04-06T18:18:32Z","snapshot_observed_at":"2026-08-16T05:03:18.904793Z","submitted_at":"2026-04-06T18:18:32Z","title":"Learning Kalman Policy for Singular Unknown Covariances via Riemannian Regularization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T19:11:49.407111Z"},"links":{"cited_paper":"/paper/2507.14354","citing_paper":"/paper/2604.05071"},"observation_digest":"sha256:a79fada5b9bf7496c35d2627193712c446c9a4035d929151170ae0dbbd00e905","observation_id":"6103a443-9ba4-4681-8ff3-3d52c7563464","resolution":{"observed_at":"2026-05-10T23:20:53.693290Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2507.14354/citation-record","integrity":"/paper/2507.14354/integrity","json":"/paper/2507.14354/citation-record.json","paper":"/paper/2507.14354"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:19:37.355257Z","title":"Optimizing static linear feedback: Gradient method","venue":null,"work_id":"bf9b2e6d-75af-4e0d-8a7b-7259907c3265","year":2021},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.241689Z"},"links":{"citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:43a62f39fff1cc62e2503be4a66c25325b0de2637512f270cc9f08055cb5eeaf","observation_id":"608937a8-5fcc-423a-893a-baea080273b5","resolution":{"observed_at":"2026-08-06T16:19:37.358879Z","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":"2026-08-06T16:19:37.344552Z","title":"Global convergence of policy gradient methods for the linear quadratic regulator","venue":null,"work_id":"a4810d57-b6ee-4565-998d-1185d067c294","year":2018},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.246004Z"},"links":{"citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:83a97d20ff2219b4eb8f2586b4fd96966efb16f34374b1854923eb47d8ec5019","observation_id":"c0f9ae4a-6cfc-45a0-8d25-990e7ce7b69f","resolution":{"observed_at":"2026-08-06T16:19:37.348127Z","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":"2310.15467","last_updated":"2023-10-27T15:32:29Z","snapshot_observed_at":"2026-08-16T14:49:31.271974Z","submitted_at":"2023-10-24T02:44:35Z","title":"Policy Optimization of Finite-Horizon Kalman Filter with Unknown Noise Covariance","version":2},"cited_work":{"arxiv_id":"2310.15467","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.15467","snapshot_observed_at":"2026-08-06T16:19:37.303168Z","title":"Policy Optimization of Finite-Horizon Kalman Filter with Unknown Noise Covariance","venue":"math.OC","work_id":"3d66d9f3-c722-4f1d-809b-7c8ed2c90510","year":2023},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.249733Z"},"links":{"cited_paper":"/paper/2310.15467","citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:e0ecff5a80ea31c3188de6f37608579d4130f61c1fe3885d81f147f874f5b88b","observation_id":"b9bb9a1d-55eb-43e0-bd2f-589d68bd6ae0","resolution":{"observed_at":"2026-08-06T16:19:37.308512Z","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":"2026-08-06T16:19:37.335159Z","title":"On the linear convergence of random search for discrete-time lqr","venue":null,"work_id":"8d7e30dd-0c9e-4b38-b834-baabbc44e674","year":2020},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.253501Z"},"links":{"citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:85c9cbe8ec2a91550e5af9ff12c67aa2b9f21d6c7780ecb9a48b915100ed7658","observation_id":"de2644cb-2fc4-43f4-80d8-99f7adc920ee","resolution":{"observed_at":"2026-08-06T16:19:37.338442Z","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":"2026-08-06T16:19:37.324571Z","title":"Data-driven optimal filter- ing for linear systems with unknown noise covariances","venue":null,"work_id":"46c37651-851f-4928-96b2-2cd5a8f983bb","year":2023},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.257413Z"},"links":{"citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:daca92775905831574c5829bd11cb510b3df6cb8445e4be037c6965cc94836de","observation_id":"cedfb567-5a4e-4c83-a3fd-7859ebae31f5","resolution":{"observed_at":"2026-08-06T16:19:37.328849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04243","last_updated":"2024-06-06T16:41:10Z","snapshot_observed_at":"2026-08-16T13:45:27.128602Z","submitted_at":"2024-06-06T16:41:10Z","title":"Policy Optimization in Control: Geometry and Algorithmic Implications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04243","snapshot_observed_at":"2026-08-06T16:19:37.262239Z","title":"Pol- icy optimization in control: Geometry and algorithmic implications","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.262239Z"},"links":{"cited_paper":"/paper/2406.04243","citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:77931ff46b856e5c274b60a9c98e3c6154132b871bd23a088c463d06a01f37c9","observation_id":"9ca1fa85-e17e-4054-8e48-b4830724a22a","resolution":{"observed_at":"2026-08-06T16:19:37.262239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:19:37.315146Z","title":"Introduction to Applied Nonlinear Dynamical Systems and Chaos","venue":null,"work_id":"69d24840-bc94-479e-8c8f-8decb8e7c3cd","year":2006},"citing_paper":{"arxiv_id":"2507.14354","last_updated":"2025-07-22T14:57:57Z","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:19:37.266964Z"},"links":{"citing_paper":"/paper/2507.14354"},"observation_digest":"sha256:66c9bd684a124cfa583561fc09b1043b1f3fb981226b6f5fddfc8350c8e0ebe9","observation_id":"95f6a66d-481d-42b8-acb0-b42e08a46a5e","resolution":{"observed_at":"2026-08-06T16:19:37.318465Z","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":"2507.14354","last_updated":"2025-07-22T14:57:57Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-08-10T00:18:34.617518Z","submitted_at":"2025-07-18T20:20:06Z","title":"Interpretable Gradient Descent for Kalman Gain"},"reference_resolution":{"displayed":7,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":1,"verified_fuzzy":5},"total_outbound_references":7},"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 7 of 7 outbound references and 1 inbound Pith citation observation for arXiv:2507.14354."}