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Paper Citation Record · LEDGER

Reassessing Muon for Matrix Factorization

As of 19 August 2026, this Paper Citation Record lists 100 of 119 outbound references and 0 inbound Pith citation observations for arXiv:2607.13246.

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

pith.paper-citation-record.v1
2607.13246 v2

Coverage vector

measured 100 of 119 reference resolution

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measured 100 of 100 standing notices

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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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Reference resolution

100 of 119 outbound references displayed

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Outbound references

Observation 38b6a5e6-757d-4e21-94b6-79f274befad4 · outbound

This paper cites 2024 , note =.

Reassessing Muon for Matrix Factorization 2024 , note =

Reference 1

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Observation 9fed5329-ed6b-47d3-ab31-73f7ff3ba867 · outbound

This paper cites Muon is Scalable for LLM Training.

Reassessing Muon for Matrix Factorization Muon is Scalable for LLM Training

Reference 2

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Observation b81b85b6-5eff-4ce1-b6fc-0fea4ef56c88 · outbound

This paper cites Improved Convergence Rates of.

Reassessing Muon for Matrix Factorization Improved Convergence Rates of

Reference 3

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Observation 5e56627c-ae2f-4f13-8157-f39aa7a7c76d · outbound

This paper cites Training Deep Learning Models with Norm-Constrained LMOs , booktitle =.

Reassessing Muon for Matrix Factorization Training Deep Learning Models with Norm-Constrained LMOs , booktitle =

Reference 4

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Observation 3959e2e7-34d7-42e3-8f5a-3a172062483b · outbound

This paper cites 2022 , eprint=.

Reassessing Muon for Matrix Factorization 2022 , eprint=

Reference 5

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Observation 6e05e01b-ae55-426d-853d-4c271fa74a34 · outbound

This paper cites Proceedings of the 35th International Conference on Machine Learning,.

Reassessing Muon for Matrix Factorization Proceedings of the 35th International Conference on Machine Learning,

Reference 6

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Observation 67de6294-c29f-420c-8aa5-1b7b080a877c · outbound

This paper cites Preconditioning Benefits of Spectral Orthogonalization in.

Reassessing Muon for Matrix Factorization Preconditioning Benefits of Spectral Orthogonalization in

Reference 7

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Observation b211665c-e344-49fd-a601-177160bd99f4 · outbound

This paper cites REAM: Merging Improves Pruning of Experts in LLMs.

Reassessing Muon for Matrix Factorization REAM: Merging Improves Pruning of Experts in LLMs

Reference 8

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Observation 460cb6f9-e883-47d5-81ec-5083e7aa9c7f · outbound

This paper cites New Perspectives in Graph Machine Learning , year=.

Reassessing Muon for Matrix Factorization New Perspectives in Graph Machine Learning , year=

Reference 9

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Observation 9e516bb9-43bb-487f-b30c-c36496448720 · outbound

This paper cites International conference on machine learning , pages=.

Reassessing Muon for Matrix Factorization International conference on machine learning , pages=

Reference 10

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Observation 95192450-50da-42f0-859e-d1a086ab72c3 · outbound

This paper cites Expressive power of recurrent neural networks.

Reassessing Muon for Matrix Factorization Expressive power of recurrent neural networks

Reference 11

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Observation 1e664831-5642-4d49-b59b-3d9d1f4502dd · outbound

This paper cites Probabilistic Tiny Recursive Model.

Reassessing Muon for Matrix Factorization Probabilistic Tiny Recursive Model

Reference 12

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Observation 24776180-9905-48de-a3fb-f4244503ce11 · outbound

This paper cites Conference on learning theory , pages=.

Reassessing Muon for Matrix Factorization Conference on learning theory , pages=

Reference 13

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Observation cc3c7ae8-aca8-40c1-9e17-61cc046db777 · outbound

This paper cites Advances in neural information processing systems , volume=.

Reassessing Muon for Matrix Factorization Advances in neural information processing systems , volume=

Reference 14

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Observation f15dc13a-07e9-4edb-ba90-ff5c91a74823 · outbound

This paper cites A Note on the Convergence of Muon.

Reassessing Muon for Matrix Factorization A Note on the Convergence of Muon

Reference 15

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Observation 2942369d-0b12-4d62-b9fb-938b1af576fd · outbound

This paper cites Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs).

Reassessing Muon for Matrix Factorization Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)

Reference 16

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Observation a2631f6a-5480-4151-9a89-e0d1a62c0bb9 · outbound

This paper cites On the Convergence Analysis of Muon.

Reassessing Muon for Matrix Factorization On the Convergence Analysis of Muon

Reference 17

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Observation a2eddc23-d574-4744-acf2-311491f98d8c · outbound

This paper cites CoRR , volume =.

Reassessing Muon for Matrix Factorization CoRR , volume =

Reference 18

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Observation 0280ecb4-bc47-4bba-a6a4-cd6b3a378448 · outbound

This paper cites Convergence Bound and Critical Batch Size of Muon Optimizer.

Reassessing Muon for Matrix Factorization Convergence Bound and Critical Batch Size of Muon Optimizer

Reference 19

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Observation 80e8d635-8e9e-4c9a-9e4f-c2b752cd2a76 · outbound

This paper cites Journal of Machine Learning Research , year =.

Reassessing Muon for Matrix Factorization Journal of Machine Learning Research , year =

Reference 20

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Observation 81d0fd97-a1dd-46e0-88c6-30656c12a409 · outbound

This paper cites Proceedings of the 36th International Conference on Machine Learning , pages =.

Reassessing Muon for Matrix Factorization Proceedings of the 36th International Conference on Machine Learning , pages =

Reference 21

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Observation 9c27d578-07c6-479a-ab6b-aa00fd2b2b8d · outbound

This paper cites Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond , journal =.

Reassessing Muon for Matrix Factorization Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond , journal =

Reference 22

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Reassessing Muon for Matrix Factorization Unresolved cited work

Reference 23

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Observation d7d0365f-9144-4f47-8fa8-041ac59290fd · outbound

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Reassessing Muon for Matrix Factorization Investigating the Overlooked Hessian Structure: From

Reference 24

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Reassessing Muon for Matrix Factorization 2022 , isbn =

Reference 25

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Observation 366f1d69-2de7-4786-ac7d-149cc0e6f122 · outbound

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Reassessing Muon for Matrix Factorization Practical Efficiency of Muon for Pretraining

Reference 26

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This paper cites Proceedings of the 39th International Conference on Machine Learning , pages =.

Reassessing Muon for Matrix Factorization Proceedings of the 39th International Conference on Machine Learning , pages =

Reference 27

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Reassessing Muon for Matrix Factorization 2019 , eprint=

Reference 28

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Reassessing Muon for Matrix Factorization 2019 , url=

Reference 29

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Reassessing Muon for Matrix Factorization Unresolved cited work

Reference 30

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This paper cites Proceedings of the 37th International Conference on Machine Learning , pages =.

Reassessing Muon for Matrix Factorization Proceedings of the 37th International Conference on Machine Learning , pages =

Reference 31

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This paper cites Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization.

Reassessing Muon for Matrix Factorization Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization

Reference 32

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Reassessing Muon for Matrix Factorization Beyond the Ideal: Analyzing the Inexact

Reference 33

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Reassessing Muon for Matrix Factorization Error Feedback for

Reference 34

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Reassessing Muon for Matrix Factorization Convergence of

Reference 35

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Reassessing Muon for Matrix Factorization CoRR , volume =

Reference 36

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Reassessing Muon for Matrix Factorization CoRR , volume =

Reference 37

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Observation 42a2f19b-773b-4556-8d13-880fdb8b5a2c · outbound

This paper cites The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm.

Reassessing Muon for Matrix Factorization The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm

Reference 38

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Reassessing Muon for Matrix Factorization 2025 , eprint=

Reference 39

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Reassessing Muon for Matrix Factorization 2024 , url =

Reference 40

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Observation b61f662c-926f-4581-b059-06b77a36ceba · outbound

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Reassessing Muon for Matrix Factorization Rakhuba , title =

Reference 41

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source=arxiv_source observed=2026-08-04T04:22:57.930307Z digest=sha256:5e1455663227a1c583efbde7678050d4b95bedb9d7be2103a415776d23b8615a

Observation caa8a6de-8622-4312-969f-591e0a73e203 · outbound

This paper cites 2025 , month =.

Reassessing Muon for Matrix Factorization 2025 , month =

Reference 42

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source=arxiv_source observed=2026-08-04T04:22:57.967373Z digest=sha256:365bd596bcf373a99c54f7c1d9c63cf12a41812767cb1025ac2321ad3dbc684b

Observation 10bead1b-be19-45db-a86c-54d59591580b · outbound

This paper cites PolarGrad:.

Reassessing Muon for Matrix Factorization PolarGrad:

Reference 43

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source=arxiv_source observed=2026-08-04T04:22:58.041231Z digest=sha256:4f694f4566afc5214dbe68b7b75498c9c8833191438284695074f19ab5256125

Observation facbeb86-2c83-460e-954b-101bc074d3a6 · outbound

This paper cites 2025 , url =.

Reassessing Muon for Matrix Factorization 2025 , url =

Reference 44

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source=arxiv_source observed=2026-08-04T04:22:58.097696Z digest=sha256:e786127b739fb3d37bbf67cf6590f1e4d759192bef427c998a89a466e68dec52

Observation 7c28d257-6701-4708-9ad4-85941e3c8418 · outbound

This paper cites Advances in neural information processing systems , volume=.

Reassessing Muon for Matrix Factorization Advances in neural information processing systems , volume=

Reference 45

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source=arxiv_source observed=2026-08-04T04:22:58.216141Z digest=sha256:24f8a379138e47259626d5155da3c4f95aa2b5bdf62fbb7201dcca24cd37cf86

Observation 184d7e0e-f226-4909-988a-83d4f3e7645b · outbound

This paper cites Advances in neural information processing systems , volume=.

Reassessing Muon for Matrix Factorization Advances in neural information processing systems , volume=

Reference 46

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source=arxiv_source observed=2026-08-04T04:22:58.277077Z digest=sha256:519eb393634f3de82f631dbabb27ae3edea14044c0f974785b0ca8fa29c5369c

Observation eaed8ff4-ec13-4adb-bfc3-5d9fa67d84e4 · outbound

This paper cites 2025 , eprint=.

Reassessing Muon for Matrix Factorization 2025 , eprint=

Reference 47

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source=arxiv_source observed=2026-08-04T04:22:58.391684Z digest=sha256:adacb87b4e2b015a7358e3fc0c1bf4adcc7d6f7e368f6cf742a5a95595144a82

Observation c25b7eca-9696-466d-aec6-9679815edb44 · outbound

This paper cites Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training.

Reassessing Muon for Matrix Factorization Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training

Reference 48

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no resolver link, observed 2026-08-04T04:22:58.527258Z

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source=arxiv_source observed=2026-08-04T04:22:58.527258Z digest=sha256:733d8c6067eb0bc1701dc9634d7d3cf9ccea1b5416f72e2919a88d8e00f7603f

Observation 4dc5550f-2909-4632-a1d0-3168a7edb678 · outbound

This paper cites LiMuon: Light and Fast Muon Optimizer for Large Models.

Reassessing Muon for Matrix Factorization LiMuon: Light and Fast Muon Optimizer for Large Models

Reference 49

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source=arxiv_source observed=2026-08-04T04:22:58.655166Z digest=sha256:aac053e4ae3a3fd75ad7947a4183e6ef8b855f48f492171c10b0bb63c7ab917b

Observation 53fb57a2-8496-442b-84c6-c721e546838d · outbound

This paper cites CoRR , volume =.

Reassessing Muon for Matrix Factorization CoRR , volume =

Reference 50

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source=arxiv_source observed=2026-08-04T04:22:58.799965Z digest=sha256:62af7b8ac7a84108c3bffd55ea1d409b12ebc21d074942ef94d55a5c721fe1c9

Observation 74eb4fc5-552f-4a0d-9c89-36ff3a6e970f · outbound

This paper cites CoRR , volume =.

Reassessing Muon for Matrix Factorization CoRR , volume =

Reference 51

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source=arxiv_source observed=2026-08-04T04:22:58.886599Z digest=sha256:7a15fa1d1dcf21184d3a919e10de6f4c1cb28f33675280394d6ba197e2df073b

Observation 5f0d1b9b-152d-4663-95b5-c84ed6698fde · outbound

This paper cites Why transformers need.

Reassessing Muon for Matrix Factorization Why transformers need

Reference 52

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no resolver link, observed 2026-08-04T04:22:58.929432Z

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source=arxiv_source observed=2026-08-04T04:22:58.929432Z digest=sha256:e45a23a00764b38a7851dd3853c89a79b2452425838618b7ad2381522b263625

Observation c534ce25-b153-4d56-b4ba-c0a8fa801c6c · outbound

This paper cites Provable acceleration of.

Reassessing Muon for Matrix Factorization Provable acceleration of

Reference 53

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no resolver link, observed 2026-08-04T04:22:59.073864Z

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source=arxiv_source observed=2026-08-04T04:22:59.073864Z digest=sha256:9f9d5a78a6aedb26659f2412536c7b4bcac5acde9100cabe5b3c5dc502c147b4

Observation ff2e0f2d-8dcf-4c3a-ada9-1161ebc7988f · outbound

This paper cites Conference On Learning Theory , pages=.

Reassessing Muon for Matrix Factorization Conference On Learning Theory , pages=

Reference 54

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source=arxiv_source observed=2026-08-04T04:22:59.178666Z digest=sha256:530b107bd2f7409412a9be76234b5cc038755e538d27de578bdd5b5787e8ab45

Observation 86d2a74d-508f-4c13-a673-e5c2d70968b0 · outbound

This paper cites Old Optimizer, New Norm: An Anthology.

Reassessing Muon for Matrix Factorization Old Optimizer, New Norm: An Anthology

Reference 55

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no resolver link, observed 2026-08-04T04:22:59.385571Z

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source=arxiv_source observed=2026-08-04T04:22:59.385571Z digest=sha256:6545f005e088c11627f99d82b5ac9293e6631fc71250f88f2c5df29a5e11471a

Observation 3a38f9a8-894b-486e-91fc-d6197668abab · outbound

This paper cites Advances in neural information processing systems , volume=.

Reassessing Muon for Matrix Factorization Advances in neural information processing systems , volume=

Reference 56

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source=arxiv_source observed=2026-08-04T04:22:59.470850Z digest=sha256:bbd6da79461900d193b2026dfcb433a0eb33bfbe6acee656a6da9c2b21024036

Observation c0e8e19b-fd47-4c3e-ae8c-a3a0a36d81a1 · outbound

This paper cites Training deep learning models with norm-constrained.

Reassessing Muon for Matrix Factorization Training deep learning models with norm-constrained

Reference 57

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source=arxiv_source observed=2026-08-04T04:22:59.581391Z digest=sha256:ae79fdd978680c25d2d85f248e4688a7bfb3b5b1e677091a677f7b4824f5e2b2

Observation 0c29f86f-0a71-49a2-9fd7-4a87276d0e3e · outbound

This paper cites BIT Numerical Mathematics , volume=.

Reassessing Muon for Matrix Factorization BIT Numerical Mathematics , volume=

Reference 58

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source=arxiv_source observed=2026-08-04T04:22:59.685113Z digest=sha256:ad9aedbdca8a809e926878af9a16b7513bb97a39ee0c69b04e81a80a6de5d524

Observation ca4848b5-1a75-49d6-b4d5-6f6f3facacc0 · outbound

This paper cites Proceedings of the International Congress of Mathematicians 2010 , pages=.

Reassessing Muon for Matrix Factorization Proceedings of the International Congress of Mathematicians 2010 , pages=

Reference 59

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source=arxiv_source observed=2026-08-04T04:22:59.785451Z digest=sha256:7f20f0045e212e94f59b406fe4465f616f1d1ffb0fce1563e2c3632dbb721903

Observation 2ba82fc1-340d-4ea6-957f-a9d7c37a26ad · outbound

This paper cites Numerical Linear Algebra with Applications , volume=.

Reassessing Muon for Matrix Factorization Numerical Linear Algebra with Applications , volume=

Reference 60

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source=arxiv_source observed=2026-08-04T04:22:59.845639Z digest=sha256:e32077fa9864ff075f589da0f27806a675d945de016c5dd2b2514688685ae2e6

Observation 03a36047-7038-4b16-a93e-c03551b76e45 · outbound

This paper cites International Conference on Machine Learning , pages=.

Reassessing Muon for Matrix Factorization International Conference on Machine Learning , pages=

Reference 61

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source=arxiv_source observed=2026-08-04T04:22:59.945747Z digest=sha256:f268e0299c607a3355182bb05907038f1791d12c2d3483ae6122fc7dbdb93db2

Observation 490d03bf-38a2-4557-b10d-e575f7facb21 · outbound

This paper cites 2018 , organization=.

Reassessing Muon for Matrix Factorization 2018 , organization=

Reference 62

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source=arxiv_source observed=2026-08-04T04:23:00.002688Z digest=sha256:5a8d811b2cdf492b595ba940df9486f484a0229920fae2e0e56410ab5aa9e7c7

Observation 74ac2714-614b-403b-87c6-357045a00c62 · outbound

This paper cites Stat , volume=.

Reassessing Muon for Matrix Factorization Stat , volume=

Reference 63

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no resolver link, observed 2026-08-04T04:23:00.161269Z

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source=arxiv_source observed=2026-08-04T04:23:00.161269Z digest=sha256:3c1b9d9f9e1d6665d986403dd542a2f73b0ccb758dc1b1c57db84f48df5e4168

Observation c3779ddc-d005-450f-9dd5-d186689178cb · outbound

This paper cites SIAM journal on Matrix Analysis and Applications , volume=.

Reassessing Muon for Matrix Factorization SIAM journal on Matrix Analysis and Applications , volume=

Reference 64

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source=arxiv_source observed=2026-08-04T04:23:00.319579Z digest=sha256:d12b975905c7b45971ce44e46c370a3af5c4379d16b3ffd202d54d14c599bf59

Observation c31e415c-2e00-49bb-bd58-0e6cd8d859cc · outbound

This paper cites 2013 , publisher=.

Reassessing Muon for Matrix Factorization 2013 , publisher=

Reference 65

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source=arxiv_source observed=2026-08-04T04:23:00.460540Z digest=sha256:5a21207ddb5ec974de622f9d1abf60a6963fada776d6c4762681e721c75a9771

Observation 3d4e1a48-13fa-4285-8e92-33ef1b319b1d · outbound

This paper cites SIAM Journal on Matrix Analysis and Applications , volume=.

Reassessing Muon for Matrix Factorization SIAM Journal on Matrix Analysis and Applications , volume=

Reference 66

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source=arxiv_source observed=2026-08-04T04:23:00.581048Z digest=sha256:8688b505d76ec4788c51d5f5a1b65bb25adca6d0ff34c38d0b82fee74d4a1a25

Observation 3f1cc495-5a10-4c77-b3ec-2ff5a421f1bc · outbound

This paper cites SIAM journal on matrix analysis and applications , volume=.

Reassessing Muon for Matrix Factorization SIAM journal on matrix analysis and applications , volume=

Reference 67

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source=arxiv_source observed=2026-08-04T04:23:00.656399Z digest=sha256:6d3dcac5618d8b42ae9d416509f29b7ae996b86c4e59da0b9b471d9a490912b5

Observation 3773977a-28d1-4cee-b341-83bd46971ade · outbound

This paper cites Modular Duality in Deep Learning.

Reassessing Muon for Matrix Factorization Modular Duality in Deep Learning

Reference 68

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source=arxiv_source observed=2026-08-04T04:23:00.756777Z digest=sha256:4dfbcfc2e0d8636564554d2ab6e3b09748091b5938214083d35115860b4c05e8

Observation 3b13c636-ac56-4651-8bf8-1504220bc28a · outbound

This paper cites IEEE Journal of Selected Topics in Signal Processing , volume=.

Reassessing Muon for Matrix Factorization IEEE Journal of Selected Topics in Signal Processing , volume=

Reference 69

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source=arxiv_source observed=2026-08-04T04:23:00.857005Z digest=sha256:9e1bd75ad7d96936736b57793eb6e2eb5365b16d66100fccb9b76d06fa6e128b

Observation 98bbe20b-8ff0-416d-bf16-418d06236184 · outbound

This paper cites Transformers are.

Reassessing Muon for Matrix Factorization Transformers are

Reference 70

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source=arxiv_source observed=2026-08-04T04:23:00.939373Z digest=sha256:52c77dabf0a65ce601b2cb1f75be509b3d9a0ba498fa1d3b0df68822e7c05f88

Observation d93a480c-6577-49ba-b856-b5bad0bc7a6b · outbound

This paper cites 2008 , publisher=.

Reassessing Muon for Matrix Factorization 2008 , publisher=

Reference 71

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no resolver link, observed 2026-08-04T04:23:01.010264Z

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source=arxiv_source observed=2026-08-04T04:23:01.010264Z digest=sha256:ce6c393f876a06c47732fc5ba9d429980ab8e54d4b8a092f7d1e9ba87867f351

Observation 9026f80f-0a4e-4d51-97d3-0f37cc695cd3 · outbound

This paper cites Stochastic spectral descent for restricted.

Reassessing Muon for Matrix Factorization Stochastic spectral descent for restricted

Reference 72

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no resolver link, observed 2026-08-04T04:23:01.110173Z

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source=arxiv_source observed=2026-08-04T04:23:01.110173Z digest=sha256:99d8b902a2d62f5124985669ddd7abafdaae27428c21f8809442cfbc0304da1c

Observation 255b4c59-575c-4ab9-801a-983a749a9932 · outbound

This paper cites Orthogonalising gradients to speed up neural network optimisation.

Reassessing Muon for Matrix Factorization Orthogonalising gradients to speed up neural network optimisation

Reference 73

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no resolver link, observed 2026-08-04T04:23:01.225267Z

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source=arxiv_source observed=2026-08-04T04:23:01.225267Z digest=sha256:4612ea6cfc1c68b772df8cc213180c9fa2b93645c9840b096c187f1dfb62ae5e

Observation d7aeb7f8-b799-4088-b80f-36f79f4ad905 · outbound

This paper cites Muon Optimizer Accelerates Grokking.

Reassessing Muon for Matrix Factorization Muon Optimizer Accelerates Grokking

Reference 74

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source=arxiv_source observed=2026-08-04T04:23:01.281987Z digest=sha256:9514e67e449381ca1eb7bc35e4d29c2518cead32cec9a8656a4c41ad6b90b6ae

Observation 08815c80-51c8-4df3-9cc8-3b0ddb268db1 · outbound

This paper cites How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization.

Reassessing Muon for Matrix Factorization How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization

Reference 75

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source=arxiv_source observed=2026-08-04T04:23:01.340425Z digest=sha256:f6dc5e51c75b419f7d1a0c9184e53f8ffb7e79ab9c1c442ba0e50b83426743e9

Observation dfef4bc6-4f27-4987-9a6b-06a1c3654fe8 · outbound

This paper cites The Polar Express: Optimal Matrix Sign Methods and Their Application to the.

Reassessing Muon for Matrix Factorization The Polar Express: Optimal Matrix Sign Methods and Their Application to the

Reference 76

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no resolver link, observed 2026-08-04T04:23:01.494017Z

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source=arxiv_source observed=2026-08-04T04:23:01.494017Z digest=sha256:810c23b3805af6d753cfc80ce4ebeb9f7bf7c7014cd3dad99a691bf2e9b6feba

Observation 4d8264af-5831-4483-ba98-2c7794ab893e · outbound

This paper cites Practical efficiency of.

Reassessing Muon for Matrix Factorization Practical efficiency of

Reference 77

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source=arxiv_source observed=2026-08-04T04:23:01.626731Z digest=sha256:2a1efbeb9e43dc70e73f74744a4f29b36ed0a1a9591967b46afc5528e568f3e5

Observation 260f1e34-85a4-4c36-a1e1-cc4082c07b81 · outbound

This paper cites Understanding.

Reassessing Muon for Matrix Factorization Understanding

Reference 78

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source=arxiv_source observed=2026-08-04T04:23:01.753049Z digest=sha256:929f59beaa92290c2c57d00bee61202bb281ebf166b07394895c7164f707000a

Observation 5a38254d-df8d-4ee6-9b9a-76e4210bf952 · outbound

This paper cites Towards quantifying the preconditioning effect of.

Reassessing Muon for Matrix Factorization Towards quantifying the preconditioning effect of

Reference 79

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no resolver link, observed 2026-08-04T04:23:01.905231Z

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source=arxiv_source observed=2026-08-04T04:23:01.905231Z digest=sha256:120235e54689d92f3bff6e155d5f781a1107d805aa2bf9912109f16ba13af321

Observation 5512bc06-639e-46bb-ac2b-6717516fa8eb · outbound

This paper cites On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning.

Reassessing Muon for Matrix Factorization On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning

Reference 80

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no resolver link, observed 2026-08-04T04:23:02.017903Z

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source=arxiv_source observed=2026-08-04T04:23:02.017903Z digest=sha256:5c7e56805f321b977c51c36b8c8fc3568d30b858fa3c0bedae58d96a7c6b2ca3

Observation 33390da9-92a2-4598-ba2d-821b3992b6be · outbound

This paper cites Muon is scalable for.

Reassessing Muon for Matrix Factorization Muon is scalable for

Reference 81

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no resolver link, observed 2026-08-04T04:23:02.197418Z

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source=arxiv_source observed=2026-08-04T04:23:02.197418Z digest=sha256:f66411b3de57065f89dc892277172bcbe5fa695b755fb698a33121e8d509113d

Observation 45f06328-8132-443f-ab00-ce6a4d07b68c · outbound

This paper cites Implicit Bias of Spectral Descent and.

Reassessing Muon for Matrix Factorization Implicit Bias of Spectral Descent and

Reference 82

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no resolver link, observed 2026-08-04T04:23:02.338644Z

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source=arxiv_source observed=2026-08-04T04:23:02.338644Z digest=sha256:c204bca004fc8c029008da4cd78d088c8cf684fa9f7ee3392a68485d49dc76c1

Observation 39978922-e125-4481-b72d-8c382f120f1a · outbound

This paper cites 2013 , publisher =.

Reassessing Muon for Matrix Factorization 2013 , publisher =

Reference 83

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source=arxiv_source observed=2026-08-04T04:23:02.525034Z digest=sha256:0b89ca3ea3bbbb5d1d73578479e87475b76eb182a5f43d518ad82f02b8817c55

Observation 8d8da7b8-9376-445c-a052-e1ad659b0f49 · outbound

This paper cites an unresolved cited work.

Reassessing Muon for Matrix Factorization Unresolved cited work

Reference 84

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Observation c373292c-8249-4bae-b590-8fb48ae8aa21 · outbound

This paper cites A Note on the Convergence of.

Reassessing Muon for Matrix Factorization A Note on the Convergence of

Reference 85

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Observation 792b5982-dd80-40c4-8939-421e585312a9 · outbound

This paper cites Explicit formulas for the joint third and fourth central moments of the multinomial distribution.

Reassessing Muon for Matrix Factorization Explicit formulas for the joint third and fourth central moments of the multinomial distribution

Reference 86

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This paper cites Advances in Neural Information Processing Systems , volume=.

Reassessing Muon for Matrix Factorization Advances in Neural Information Processing Systems , volume=

Reference 87

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This paper cites Beyond the ideal: Analyzing the inexact.

Reassessing Muon for Matrix Factorization Beyond the ideal: Analyzing the inexact

Reference 88

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This paper cites an unresolved cited work.

Reassessing Muon for Matrix Factorization Unresolved cited work

Reference 89

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This paper cites Foundations and Trends.

Reassessing Muon for Matrix Factorization Foundations and Trends

Reference 90

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This paper cites Journal of Machine Learning Research , volume=.

Reassessing Muon for Matrix Factorization Journal of Machine Learning Research , volume=

Reference 91

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This paper cites International Conference on Machine Learning , pages=.

Reassessing Muon for Matrix Factorization International Conference on Machine Learning , pages=

Reference 92

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This paper cites Preconditioned Gradient Descent for Overparameterized Nonconvex.

Reassessing Muon for Matrix Factorization Preconditioned Gradient Descent for Overparameterized Nonconvex

Reference 93

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Observation bd7908ed-202a-4103-a369-88c0583970cb · outbound

This paper cites Foundations and Trends.

Reassessing Muon for Matrix Factorization Foundations and Trends

Reference 94

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This paper cites On the convergence analysis of.

Reassessing Muon for Matrix Factorization On the convergence analysis of

Reference 95

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Reassessing Muon for Matrix Factorization Advances in Neural Information Processing Systems , volume=

Reference 96

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Observation 41f7b3af-5a79-492f-af10-580119fc1b55 · outbound

This paper cites Muon Outperforms.

Reassessing Muon for Matrix Factorization Muon Outperforms

Reference 97

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This paper cites International Conference on Learning Representations , year=.

Reassessing Muon for Matrix Factorization International Conference on Learning Representations , year=

Reference 98

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This paper cites International Conference on Learning Representations , year=.

Reassessing Muon for Matrix Factorization International Conference on Learning Representations , year=

Reference 99

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Observation ce417a14-eb4d-400d-8945-4f731974707c · outbound

This paper cites Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization.

Reassessing Muon for Matrix Factorization Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization

Reference 100

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