Paper Citation Record · LEDGER

Policy Optimization in Control: Geometry and Algorithmic Implications

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.04243.

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

pith.paper-citation-record.v1
2406.04243 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:19:37.262239Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T17:11:40.216452Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

Interpretable Gradient Descent for Kalman Gain cites this paper.

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 9fe542c5-0ced-4500-9198-220ddb612192 · inbound

A Proximal Descent Method for Minimizing Weakly Convex Optimization cites this paper.

A Proximal Descent Method for Minimizing Weakly Convex Optimization Policy Optimization in Control: Geometry and Algorithmic Implications

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T11:32:37.550404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31a7be1c-f28a-40bc-8446-67c3367d057b · inbound

Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective cites this paper.

Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective Policy Optimization in Control: Geometry and Algorithmic Implications

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:11:40.220416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07107d1e-cfcd-43da-b090-dfb2d225cc2d · inbound

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

Learning Kalman Policy for Singular Unknown Covariances via Riemannian Regularization Policy Optimization in Control: Geometry and Algorithmic Implications

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:20:53.715224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.