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Paper Citation Record · LEDGER

On the Gradient Domination of the LQG Problem

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2507.09026.

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

pith.paper-citation-record.v1
2507.09026 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:20:57.362845Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T09:58:20.309479Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:27:56.988941Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact1
  • verified fuzzy25
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20dd2440-df1e-4dfb-ad45-c754e3b0aee8 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation,.

On the Gradient Domination of the LQG Problem Policy gradient methods for reinforcement learning with function approximation,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.475693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.050828Z digest=sha256:1dcc560560e4918965a1bce0e51515c74d487465bee6037a74d67d478c6905d3

Observation c64ca0d2-5256-48e7-9030-a933e6fdaf62 · outbound

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

On the Gradient Domination of the LQG Problem Global convergence of policy gradient methods for the linear quadratic regulator,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.437041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.059637Z digest=sha256:8d369380b26f3d6e2a227da4e5ae281af070dd6bdb5bd9f136ddd584169ce61c

Observation 6a0d64cd-fa73-4561-970a-694932e44866 · outbound

This paper cites Gradient methods for minimizing functionals,.

On the Gradient Domination of the LQG Problem Gradient methods for minimizing functionals,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.407843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.066582Z digest=sha256:407121d5742fc3f54cfbd8d23f720329bd8f37ff931830e1ff900068d9e40943

Observation b538a826-711c-4f11-8c31-8c23cc3fe5f7 · outbound

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

On the Gradient Domination of the LQG Problem On the linear convergence of random search for discrete-time LQR,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.373854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.073732Z digest=sha256:f061bc98ccb3f3725f0229dd4beb141f8c7b20df276a281c44ede9d13976f02e

Observation b9ce5783-4151-4c17-bbf4-a16a63ec3c88 · outbound

This paper cites Toward a theoretical foundation of policy optimization for learning control policies,.

On the Gradient Domination of the LQG Problem Toward a theoretical foundation of policy optimization for learning control policies,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.344660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.086028Z digest=sha256:8bc58ceb0a08fd8429415058e36683cad483662ac3d009c1da6d6ce8d043a6bd

Observation 75047d35-46da-46c6-9503-c11a917970da · outbound

This paper cites Policy optimization for H2 linear control with H∞ robustness guarantee: Implicit regularization and global convergence,.

On the Gradient Domination of the LQG Problem Policy optimization for H2 linear control with H∞ robustness guarantee: Implicit regularization and global convergence,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.307247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.099048Z digest=sha256:bb37907de094848101527a6c73653f199f26928cab11a8b56a565d76fe4b1937

Observation fb54abb1-e96f-433b-8e17-89364af7bf2a · outbound

This paper cites Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach.

On the Gradient Domination of the LQG Problem Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T18:20:57.109819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:57.109819Z digest=sha256:3b99a85aa370ae6ed0cfdb3d085c17143f7f402285df35749b9ae5222c44943b

Observation 0404f2ec-4551-4210-8d82-c4ff0ee8eb10 · outbound

This paper cites Robot Fleet Learning via Policy Merging.

On the Gradient Domination of the LQG Problem Robot Fleet Learning via Policy Merging

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T18:20:57.122192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:57.122192Z digest=sha256:eac1cb0581a13c161ba680a8fd40238faed67d4a10925736fdd0854714868fc2

Observation 6c8a69c6-d903-4a77-8f29-6f2bf66e68e9 · outbound

This paper cites Meta-learning linear quadratic regulators: A policy gradient MAML approach for model-free LQR,.

On the Gradient Domination of the LQG Problem Meta-learning linear quadratic regulators: A policy gradient MAML approach for model-free LQR,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.263071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.134798Z digest=sha256:4e0b4a828ae1352aaa1f6f6d8f5a4ce36e3f4a4e4c31df85005e19effcefb63c

Observation f129d589-0c70-4fce-9cee-63627b49746a · outbound

This paper cites Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning.

On the Gradient Domination of the LQG Problem Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:20:57.142267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:57.142267Z digest=sha256:37feaab77b8442ea4398d64ac3f91609fe6b17c8fbefd0d57854e8eb681f4f2b

Observation b2d8965b-cbf0-4dd8-8278-e14af4ff2cce · outbound

This paper cites On the Convergence of Policy Gradient for Designing a Linear Quadratic Regulator by Leveraging a Proxy System,.

On the Gradient Domination of the LQG Problem On the Convergence of Policy Gradient for Designing a Linear Quadratic Regulator by Leveraging a Proxy System,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.221606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.151500Z digest=sha256:1a035c33207a9d3e579b16e35b18c5e2bb5b77feef9ef5ecd0fe5a18b6494171

Observation fde2d63b-ac49-4e84-b2ae-bdef3d0d896c · outbound

This paper cites Globally convergent policy gradient methods for linear quadratic control of partially observed systems,.

On the Gradient Domination of the LQG Problem Globally convergent policy gradient methods for linear quadratic control of partially observed systems,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.186606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.159760Z digest=sha256:297e52cba7460813506bd5b4aee9a3bc878abfb45357dedfd7c822b4f68f5383

Observation a4712932-dafc-4ba1-82e8-b6024a3d90dd · outbound

This paper cites On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information,.

On the Gradient Domination of the LQG Problem On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.150003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.170227Z digest=sha256:c02fb9ba7d8ef6899a8a131485b96b8ae5a7d8ac79ebd1653fc56bfc0e1d783c

Observation abf11ceb-e75a-4b74-b6cc-36a1d811c6bd · outbound

This paper cites Analysis of the optimization landscape of linear quadratic Gaussian (LQG) control,.

On the Gradient Domination of the LQG Problem Analysis of the optimization landscape of linear quadratic Gaussian (LQG) control,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.119418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.182980Z digest=sha256:6ac9eefa3278a0564e9a835e127faba5ccbf1db306762cbc6c934aa0d91801e4

Observation 76df692f-d692-4468-897d-8ed9dd92f904 · outbound

This paper cites Behavioral feedback for optimal LQG control,.

On the Gradient Domination of the LQG Problem Behavioral feedback for optimal LQG control,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.074014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.192550Z digest=sha256:d0785f54c0e219008ccb0bc25b7e85641d6df6155c78526d187eb4782fae72a7

Observation c11b3a31-a3b1-41cd-a389-302f98f1354f · outbound

This paper cites Imitation and transfer learning for LQG control,.

On the Gradient Domination of the LQG Problem Imitation and transfer learning for LQG control,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.046474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.201746Z digest=sha256:c5427a9c15a41f429aa41dd80e912449a17d47b31b69803a5ed37e4ffc9bb1c2

Observation fb049bcc-53cc-4987-a4f4-b8c63fb2cfdf · outbound

This paper cites The data-based LQG control problem,.

On the Gradient Domination of the LQG Problem The data-based LQG control problem,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:58.024365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.212811Z digest=sha256:96da6c47b682dd21f4f9959c5bb350ef3a57adb0cbb43076e45c058f7017fb0b

Observation 49042bde-3c1e-4a8d-a136-bf7234a57d0c · outbound

This paper cites an unresolved cited work.

On the Gradient Domination of the LQG Problem Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:20:57.988795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.228084Z digest=sha256:b5195eb2996bdbb5b923a2be4024e3916739ec54d4a15750a368643acd92f273

Observation 5aba3b42-6109-4b3a-b2fd-9917bf793d70 · outbound

This paper cites Escaping high-order saddles in policy optimization for linear quadratic Gaussian control,.

On the Gradient Domination of the LQG Problem Escaping high-order saddles in policy optimization for linear quadratic Gaussian control,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.957431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.236553Z digest=sha256:58d04000c4edefa7d489a2b6050d65c3a647e3f90db235e407d3cefc67bd2d50

Observation 62594370-876c-415c-972f-b4fa2805752b · outbound

This paper cites Data-Driven Policy Gradient Method for Optimal Output Feedback Control of LQR,.

On the Gradient Domination of the LQG Problem Data-Driven Policy Gradient Method for Optimal Output Feedback Control of LQR,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.927882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.245689Z digest=sha256:dd175524ee056afded94e376cccaa2250ab72de000cb609c43d05ab75600e720

Observation d1dc6f3c-8f51-4203-b1bb-5122bccd34ef · outbound

This paper cites How are policy gradient methods affected by the limits of control?.

On the Gradient Domination of the LQG Problem How are policy gradient methods affected by the limits of control?

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.897984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.256125Z digest=sha256:b72104cd248cec48a31c8098dba58173fbd40753c53ac8fc2d143b2646f16b67

Observation c51d4dc3-56fd-4911-9ac8-a1e88091116c · outbound

This paper cites Learning optimal controllers for linear systems with multiplicative noise via policy gradient,.

On the Gradient Domination of the LQG Problem Learning optimal controllers for linear systems with multiplicative noise via policy gradient,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.873085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.272878Z digest=sha256:cc4797febf346da1dde73c74a5c7805889708dcef30fa685f125c8666113ae4a

Observation 66e1eeea-4eeb-4ed3-b075-a755fe698623 · outbound

This paper cites Oracle complexity reduction for model-free LQR: A stochastic variance-reduced policy gradient approach,.

On the Gradient Domination of the LQG Problem Oracle complexity reduction for model-free LQR: A stochastic variance-reduced policy gradient approach,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.844049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.281214Z digest=sha256:51cf592d191924f0856a920a360e4a6fa7c61ecba448bcb60e7b14f0376eec04

Observation faa2fa11-3d7b-4388-89c7-49b1080a641f · outbound

This paper cites Computing stabilizing linear controllers via policy iteration,.

On the Gradient Domination of the LQG Problem Computing stabilizing linear controllers via policy iteration,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.813113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.288961Z digest=sha256:47d165139bd78440ef9ce37f2f3bc34541a9c7004525a623cf3a24a63a2694b8

Observation 66b9ce1e-0f3b-40eb-a1cf-f3570417d5d7 · outbound

This paper cites Stabilizing dynamical systems via policy gradient methods,.

On the Gradient Domination of the LQG Problem Stabilizing dynamical systems via policy gradient methods,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.778133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.296038Z digest=sha256:060451e67bd39e4e0f07c0e0e63cb067a1f1709114c740759109e9829f474de0

Observation 89aaff0c-cdfe-4006-8514-55ce4574c522 · outbound

This paper cites Convergence and sample complexity of policy gradient methods for stabilizing linear systems,.

On the Gradient Domination of the LQG Problem Convergence and sample complexity of policy gradient methods for stabilizing linear systems,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.734570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.302928Z digest=sha256:27c616a9dc2433e377b71296e3cf31cda43eab135ed89c7870a4a01caa039230

Observation d9808ca7-2cab-4c20-b125-326a989a907d · outbound

This paper cites Learning Stabilizing Policies via an Unstable Subspace Representation.

On the Gradient Domination of the LQG Problem Learning Stabilizing Policies via an Unstable Subspace Representation

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:20:57.482043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.316211Z digest=sha256:cf14f62b2b466540a4568529d1a4bcdb9f22c29a3dd78fbcef5b823cddd31050

Observation 8f8ec5e0-5dbe-4cb1-9111-03c021869f90 · outbound

This paper cites Derivative-free methods for policy optimization: Guarantees for linear quadratic systems,.

On the Gradient Domination of the LQG Problem Derivative-free methods for policy optimization: Guarantees for linear quadratic systems,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.700973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.337896Z digest=sha256:3ced1e195e6b0c25d1dda18122ff874625108209f9392c2c53bcb2faaca08933

Observation 09115386-a42f-45da-8acc-6640b73d6351 · outbound

This paper cites Random gradient-free minimization of convex functions,.

On the Gradient Domination of the LQG Problem Random gradient-free minimization of convex functions,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.673678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.353648Z digest=sha256:6d59f3316b7abdaac2ca0dcda0a1e139e0eb57777b2caac2834988e406dc97f3

Observation 25008113-fc55-4363-99ef-0d9cc930aba8 · outbound

This paper cites Vershynin, High-dimensional probability: An introduction with applications in data science.

On the Gradient Domination of the LQG Problem Vershynin, High-dimensional probability: An introduction with applications in data science

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:57.647013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:20:57.362845Z digest=sha256:1b0abb2120b84ec270698336442fec13575f07789f61c376af63840c81ab5fc4

Pith citing papers

Observation 29136410-42ff-49f6-afeb-3cdfe9942ded · inbound

Multitask LQG Control: Performance and Generalization Bounds cites this paper.

Multitask LQG Control: Performance and Generalization Bounds On the Gradient Domination of the LQG Problem

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:37:04.713634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T07:34:29.215557Z digest=sha256:c57dea2131092c54e7ef5943509e692e1571d58ca5b126a73ea1ccd2018c77f8

Observation 511ac100-ed45-4aff-a7f9-1b3c689c0baf · inbound

Two-Layer Linear Auto-Regressive Models Estimate Latent States cites this paper.

Two-Layer Linear Auto-Regressive Models Estimate Latent States On the Gradient Domination of the LQG Problem

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:27:56.990591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T09:58:20.309479Z digest=sha256:a395f38e5353fd4cd78079a07504274c20e916ce17c91e594363db4692ce2072