Paper Citation Record · LEDGER

Interpretable Gradient Descent for Kalman Gain

As of 21 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 1 inbound Pith citation observation for arXiv:2507.14354.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.14354 v2

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:19:37.266964Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T19:11:49.407111Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T23:20:53.676982Z

Reference resolution

7 of 7 outbound references displayed

verified exact1
verified fuzzy5
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 608937a8-5fcc-423a-893a-baea080273b5 · outbound

This paper cites Optimizing static linear feedback: Gradient method.

Interpretable Gradient Descent for Kalman Gain Optimizing static linear feedback: Gradient method

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:37.358879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0f9ae4a-6cfc-45a0-8d25-990e7ce7b69f · outbound

This paper cites Global convergence of policy gradient methods for the linear quadratic regulator.

Interpretable Gradient Descent for Kalman Gain Global convergence of policy gradient methods for the linear quadratic regulator

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:37.348127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9bb9a1d-55eb-43e0-bd2f-589d68bd6ae0 · outbound

This paper cites Policy Optimization of Finite-Horizon Kalman Filter with Unknown Noise Covariance.

Interpretable Gradient Descent for Kalman Gain Policy Optimization of Finite-Horizon Kalman Filter with Unknown Noise Covariance

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:19:37.308512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de2644cb-2fc4-43f4-80d8-99f7adc920ee · outbound

This paper cites On the linear convergence of random search for discrete-time lqr.

Interpretable Gradient Descent for Kalman Gain On the linear convergence of random search for discrete-time lqr

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:37.338442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cedfb567-5a4e-4c83-a3fd-7859ebae31f5 · outbound

This paper cites Data-driven optimal filter- ing for linear systems with unknown noise covariances.

Interpretable Gradient Descent for Kalman Gain Data-driven optimal filter- ing for linear systems with unknown noise covariances

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:37.328849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ca1fa85-e17e-4054-8e48-b4830724a22a · outbound

This paper cites Policy Optimization in Control: Geometry and Algorithmic Implications.

Interpretable Gradient Descent for Kalman Gain Policy Optimization in Control: Geometry and Algorithmic Implications

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:37.262239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95f6a66d-481d-42b8-acb0-b42e08a46a5e · outbound

This paper cites Introduction to Applied Nonlinear Dynamical Systems and Chaos.

Interpretable Gradient Descent for Kalman Gain Introduction to Applied Nonlinear Dynamical Systems and Chaos

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:37.318465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 6103a443-9ba4-4681-8ff3-3d52c7563464 · inbound

Learning Kalman Policy for Singular Unknown Covariances via Riemannian Regularization cites this paper.

Learning Kalman Policy for Singular Unknown Covariances via Riemannian Regularization Interpretable Gradient Descent for Kalman Gain

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:20:53.693290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.