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Paper Citation Record · LEDGER

Rethinking Bregman Divergences in Kronecker-Factored Optimizers

As of 22 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2606.00542.

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

pith.paper-citation-record.v1
2606.00542 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T18:51:37.230404Z

measured 26 of 26 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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Outbound references

Observation a26cda3a-c27b-4072-b428-8251cab86500 · outbound

This paper cites ASGO : Adaptive structured gradient optimization.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers ASGO : Adaptive structured gradient optimization

Reference 1

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Observation 0f430907-0350-4330-9008-5635e73544e3 · outbound

This paper cites an unresolved cited work.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Unresolved cited work

Reference 2

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Observation 745a3180-de59-4feb-bbb5-3946fac6ed23 · outbound

This paper cites Turner, and Hao-Jun Michael Shi.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Turner, and Hao-Jun Michael Shi

Reference 3

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Observation 4153d71b-0560-4f59-86ac-75db167f5ba3 · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Shampoo: Preconditioned stochastic tensor optimization

Reference 4

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Observation f5bf783f-a1be-4e9b-9b2c-537179ce6f4e · outbound

This paper cites Gradient Descent Happens in a Tiny Subspace.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Gradient Descent Happens in a Tiny Subspace

Reference 5

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local_arxiv, observed 2026-06-28T19:52:35.336533Z

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Observation 274f45da-84d5-4d1d-b3df-fd85c48deaea · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks, 2024.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Muon: An optimizer for hidden layers in neural networks, 2024

Reference 6

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Observation 84058f33-fcce-4c74-b103-608709bd2c3f · outbound

This paper cites Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, and George E.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, and George E

Reference 7

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Observation 80c3ab90-7dac-4a56-a057-d47e07ec5932 · outbound

This paper cites arXiv preprint arXiv:2510.16981 , year=.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers arXiv preprint arXiv:2510.16981 , year=

Reference 8

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arxiv_id, observed 2026-06-28T19:52:35.330523Z

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Observation e2164c44-f190-4bf8-aeb5-317f68126685 · outbound

This paper cites Adam: A method for stochastic optimization.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Adam: A method for stochastic optimization

Reference 9

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Observation f2a6292c-7d31-4d3c-970a-4543308ae124 · outbound

This paper cites Limitations of the empirical fisher approximation for natural gradient descent.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Limitations of the empirical fisher approximation for natural gradient descent

Reference 10

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Observation 58c045b2-ed9b-4fbc-bf23-899a38e713cf · outbound

This paper cites Lowe, Felix Dangel, Runa Eschenhagen, Zikun Xu, and Roger Baker Grosse.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Lowe, Felix Dangel, Runa Eschenhagen, Zikun Xu, and Roger Baker Grosse

Reference 11

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Observation 91f9f3eb-44fa-45d1-b11c-15923fd66d42 · outbound

This paper cites Decoupled weight decay regularization.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Decoupled weight decay regularization

Reference 12

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Observation d2d7a0bd-28c8-4653-8705-afa77104c650 · outbound

This paper cites Optimizing neural networks with kronecker-factored approximate curvature.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Optimizing neural networks with kronecker-factored approximate curvature

Reference 13

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Observation 1224fc82-45f1-42a6-acee-463ebd212618 · outbound

This paper cites A new perspective on shampoo's preconditioner.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers A new perspective on shampoo's preconditioner

Reference 14

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Observation c8563f8f-840c-4fb5-a933-c4dd23cc39eb · outbound

This paper cites Does sgd really happen in tiny subspaces? In 13th International Conference on Learning Representations.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Does sgd really happen in tiny subspaces? In 13th International Conference on Learning Representations

Reference 15

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Observation e43a6d8b-94a7-484f-a71d-d8c6c280601e · outbound

This paper cites On the interplay between noise and curvature and its effect on optimization and generalization.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers On the interplay between noise and curvature and its effect on optimization and generalization

Reference 16

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Observation 4ffb2bd3-525f-4d46-86ed-b46ca3976675 · outbound

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Rethinking Bregman Divergences in Kronecker-Factored Optimizers Unresolved cited work

Reference 17

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Observation 7c5f60c7-2d40-4962-b601-6d971d4b339b · outbound

This paper cites The sharpness disparity principle in transformers for accelerating language model pre-training.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers The sharpness disparity principle in transformers for accelerating language model pre-training

Reference 18

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Observation ab861ed9-d794-4359-885e-7e28b1194e48 · outbound

This paper cites Understanding warmup-stable-decay learning rates: A river valley loss landscape view.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Understanding warmup-stable-decay learning rates: A river valley loss landscape view

Reference 19

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Observation ee9f0a48-5448-4fdb-a79e-65efb26138d1 · outbound

This paper cites The alignment property of sgd noise and how it helps select flat minima: A stability analysis.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers The alignment property of sgd noise and how it helps select flat minima: A stability analysis

Reference 20

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Observation 19809789-42de-478b-994c-9526c5ba1f71 · outbound

This paper cites Reddi, Sanjiv Kumar, and Zhiyuan Li.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Reddi, Sanjiv Kumar, and Zhiyuan Li

Reference 21

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Observation b0c8a427-3451-47d0-971c-552df71004c8 · outbound

This paper cites Controlled llm training on spectral sphere.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Controlled llm training on spectral sphere

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 53888d26-5ccc-45b9-984c-def042bda5b4 · outbound

This paper cites Fismo: Fisher-structured momentum- orthogonalized optimizer.ArXiv, abs/2601.21750.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Fismo: Fisher-structured momentum- orthogonalized optimizer.ArXiv, abs/2601.21750

Reference 23

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 3228543d-166c-44e8-a593-920d8b5d7fd4 · outbound

This paper cites Bsfa: Leveraging the subspace dichotomy to accelerate neural network training.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers Bsfa: Leveraging the subspace dichotomy to accelerate neural network training

Reference 24

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Observation eee0867e-2477-4ce7-abf3-48256660f97c · outbound

This paper cites arXiv preprint arXiv:2602.22681 , year=.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers arXiv preprint arXiv:2602.22681 , year=

Reference 25

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Observation 6db2095f-8197-4b16-8628-b5f480511d67 · outbound

This paper cites The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects.

Rethinking Bregman Divergences in Kronecker-Factored Optimizers The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects

Reference 26

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