Pith. sign in

Paper Citation Record · LEDGER

On the Gradient Domination of the LQG Problem

As of 10 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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.050828Z digest=sha256:c52ad1be01f1a3b52839aa77a7f6ff5ae1b7e15b60ffae92636c999bf89219a1

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.059637Z digest=sha256:f628dda4e370b350248ae865c73ba8c508b6e8a697f51d37b8db1a5372270520

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.066582Z digest=sha256:270ac927672f169b8bfc8b2a6fa18740c9bb90b7a231a96182f547d53843f5e9

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.073732Z digest=sha256:cc23a0e140e6f53e0b1acc08d474da0edfdd4877db35c6ea6524bb1ad36c122a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.086028Z digest=sha256:19bd3bfc47efe8b6bb44ad5282bf5c148b9816d4b5fc2d416f0ba2f62648ac4a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.099048Z digest=sha256:b53ee26750559ce67263bc143cc7b012f1a27db1464b7e20d4db44b1e4c91e07

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=sha256:090711b0761156c79b2f971ca3410885e1c06066f778759139c39e2ad13611e8

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=sha256:08e1649386336509fc3ad994d87f6128937346b17b73bb219a13c197b6fde598

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.134798Z digest=sha256:61198aeef01dacbfd991d310c73d7a8b9d1a90d6aab1bd95339c327095095559

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=sha256:0e24465927d1d5735ab2506b89a8ce41f2146af40522cb9b011b8d4893dfe369

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.151500Z digest=sha256:398e65be3536d29565253a07eec6f9473147c151da87b95224dced872b0cd510

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.159760Z digest=sha256:fa4084bcb7bde75c0e81259a3558fff504f932b0636f53eaf486416270907d7f

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.170227Z digest=sha256:cb790d9a9f9024c480bf86a3ebfea76d6000640189fac12a71fa6307b80da73f

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.182980Z digest=sha256:d2c4af1a29537d4955bf8e4fd05ee3da06979c1c42c91aade2efdcb844c9a3d4

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.192550Z digest=sha256:343a68254a64598472f1f2b08461aafe1ffce12a4f95efc495fc2515334a7808

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.201746Z digest=sha256:622ca6733ab0915c6040153fc85ba074ce17bcf79d4aa2a44f24ebe31a7b91d7

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.212811Z digest=sha256:9e8f62cdcd8927b07cb8bed886599dd4dafbd4f55ad2b9a503858011631318f0

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.228084Z digest=sha256:5a6ab4ee3acf0f214f7dec4fc4e0acb5386efd769601dd7e01ee8a128e92957d

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.236553Z digest=sha256:6f445c150f19945b2d892cb8744d57cd86635c94117a0e6836627cc4bb342ad1

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.245689Z digest=sha256:7cc7ad543bb6da84798185ac02625ce96eb7bf045b23aefb1d0237227f01856e

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.256125Z digest=sha256:6ef1684e390965858fdcb27a7e98e91d8e4aa1f26e25434019f532fbd6e0bf34

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.272878Z digest=sha256:78de0a9a3243a7101325671492633ac13b540c59235aa30ea8edb15b62c233a5

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.281214Z digest=sha256:2cf62fbeed56ddc28ef41cfcdefe32d539fb080078b03b183abf10641c5d69dd

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.288961Z digest=sha256:b357f9d37d7217e472826614186cbaf479a0f1d24041d0d0a19eaf4b5a96551d

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.296038Z digest=sha256:bf5234ef3ccfd3f86e715550bd9e35df765ac4b86c243156aa6bdee34d903ef3

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.302928Z digest=sha256:db7cb6dfce804ca3ac5bce35b782ff64c94b0ae512724129ac689d75f7ac6e58

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.316211Z digest=sha256:350901f1cd1e37ef11acc1d0ff6a9b9125f8a0c666e6ecacea80dedb9074f917

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.337896Z digest=sha256:286d9f70e34347ed86ada140095b8b089d7ed924b45fcbcb82821f4fb8aebf61

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.353648Z digest=sha256:daecad675ca4b4a4dea6901d34c9b7853df9353574bd07f588306503bb09874c

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:20:57.362845Z digest=sha256:5f8d419d322624e6792e325f37d378ffa73667604afb45cb35f336236bf80e7a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T07:34:29.215557Z digest=sha256:a3d4f4185f44ebdf35b8f730a3a26a9679e348e5cc3aaca552daa7a81ec7382c

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-27T09:58:20.309479Z digest=sha256:c808ebced7f7783e815ee212730a89bd79f40db96a32db6c792672f4127ff233