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Paper Citation Record · LEDGER

On the Gradient Domination of the LQG Problem

As of 20 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.050828Z digest=sha256:8756d1e9ee7c307a7404a8e44ed0346a5a6c1f5d530280a7c9fd8f86daa31e7b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.059637Z digest=sha256:f4175b13f9a2a02c9ef466aaed6eb7c44e22d16f35b9052bac28ad31e6640687

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.066582Z digest=sha256:38f029a46b97591ae6e3dac141cd95f2083bd5d5e541dc1c6ef357dbd2b1f01e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.073732Z digest=sha256:0b8227434f21209cebdc462a68a515499716634b1775ea7cc2c283beded18c60

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.086028Z digest=sha256:8e1ff4e85d3f55035e15cf1b425ee5046650a7a65bb30324fd42db396df1980b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.099048Z digest=sha256:3e3835f4b40364f8a450e532cd3c80149943aac31fe9990b2019282b5133d553

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=sha256:8686e82c6ab9dc983e82513e73c078fb31024b2722714bb7357c3ed3dd9b234e

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=sha256:76bbf460609b3b16978748cf653330ad0937fc4a3f3255a9c0a0520336a90bf3

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.134798Z digest=sha256:bac40b9c5f13fd9bdd8307fe1ad50a748d4657853a0473bcfa5244f8ac3162b6

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:f42b43a430e4260fb4aff576ea994ada45574cce30ab558ecc40eb990725ae4e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.151500Z digest=sha256:c9080a0ac6e5a5d0144b7dd1c8c9d864135aff08d31b092b892fa152b294c9ce

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.159760Z digest=sha256:8a07a535f1c73b64f0890cb94434a0384380b5f757a48aedb46c5832ad314454

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.170227Z digest=sha256:0a6d64f5ae406be6ac86db3f599e6dd5dd2649bd6aba59c7ffd23b7281be4b26

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.182980Z digest=sha256:c83adda02e9c87cc4de051cf6d7aecac878bb7efd93c997129056583fb51145f

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.192550Z digest=sha256:c1899b0d8a87ca7e77ec3b7c8b28668750223312ebdd33e3a31dbf95f081a8f8

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.201746Z digest=sha256:e6e8198197c57c1f225855246d8ab32b8fbf287f07356b48bd40bde3e84f8b9a

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.212811Z digest=sha256:a6b58eac5c3e4e50dd857c544cc4bd76fd98a5637e40116416e8bce25f155a5e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.228084Z digest=sha256:2039beb7afb1270cf2007e6af5eb91238f6623076313dbcfcb841105827726e6

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.236553Z digest=sha256:cba1660f918c97881623618fc00d89d3a73310c1ac213a8277e01c1aee95549c

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.245689Z digest=sha256:379a02a84fda375c998c4080bcc308a832f5c806383e07152fd1fe651d2f0da8

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.256125Z digest=sha256:5974b8a81096cb2e0de917284bd55c662bc4d08c62d8e4d1a1e8a0f8ff8f2954

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.272878Z digest=sha256:b343a869ded2240eb09e79be5d8c7a062d7a7ecc18b56be9ba91b96caa24e70b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.281214Z digest=sha256:44d23ecfc788c796def6ee75c47d3cb49e552fed4d3615ca012440c4302b9024

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.288961Z digest=sha256:53332030f51106e2811e06f1e92467066ed2c2b659e56cf76f1b802abe164f17

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.296038Z digest=sha256:a8c08b390f6a7f0566ba564338be502bd874c9315f6c88bc02cbc1fe7620e6c6

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.302928Z digest=sha256:372474a0a547bcc23f6d77ccea2b50982c274c728ef41a6052fac7056ad4c12e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.316211Z digest=sha256:3f9d3cc4f1a1ce3eac07f44c40b6e71c94cf8a9c42f7d7881fec8b9b41da8b02

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.337896Z digest=sha256:f49ff4d39a933e8190f2f4ae5d8c481c420d7ce11b27ab698dcfc61a0ab10e23

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.353648Z digest=sha256:0ae65efffcfd84b621127a9b14704d18ea50b7470b9dbb205f4af4d56d38412e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T18:20:57.362845Z digest=sha256:99139a7912c051da662f1405f34a6fa1c0613053b090ec1e723d601743fc1e3b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T07:34:29.215557Z digest=sha256:0a6f6bdbdd3eba93caa4289c4274b12fc838fae67f20f9a02b081da75401486b

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-27T09:58:20.309479Z digest=sha256:2ccc0021ffc3ca202371bc7bc3a861161a91f5b2a6c1daf8fab95d0541732dc0