Paper Citation Record · LEDGER

Optimizers Qualitatively Alter Solutions And We Should Leverage This

As of 7 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 7 inbound Pith citation observations for arXiv:2507.12224.

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

pith.paper-citation-record.v1
2507.12224 v1

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:56:21.795284Z

measured 88 of 88 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:22:15.304977Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

81 of 81 outbound references displayed

verified exact6
verified fuzzy38
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 13570ae0-c803-4c82-a256-42816735eadf · outbound

This paper cites High-dimensional dynamics of generalization error in neural networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This High-dimensional dynamics of generalization error in neural networks

Reference 1

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Observation c861194e-09b7-4c0a-9422-0e3ca5c8b8ce · outbound

This paper cites Selfless Sequential Learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Selfless Sequential Learning

Reference 2

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Observation 1ced9c68-3efb-49c0-841c-13e4a47293cb · outbound

This paper cites Learning and generalization in overparame- terized neural networks, going beyond two layers.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Learning and generalization in overparame- terized neural networks, going beyond two layers

Reference 3

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Observation c6b3db26-9fae-4090-bc2b-9192ce5efafe · outbound

This paper cites Natural gradient works efficiently in learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Natural gradient works efficiently in learning

Reference 4

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Observation bd3a1dca-2e74-4d63-807d-c75ec80a60bf · outbound

This paper cites When does preconditioning help or hurt generalization? 2020.

Optimizers Qualitatively Alter Solutions And We Should Leverage This When does preconditioning help or hurt generalization? 2020

Reference 5

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Observation 75d79d22-da28-4cff-8d4b-0e060bc218a3 · outbound

This paper cites Implicit Regularization in Deep Matrix Factorization.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Implicit Regularization in Deep Matrix Factorization

Reference 6

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Observation 469237c1-e7b6-4730-92df-245978afd23e · outbound

This paper cites Implicit Gradient Regularization.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Implicit Gradient Regularization

Reference 7

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Observation d74e641f-4675-4159-b3b4-deee2d616393 · outbound

This paper cites Reconciling modern machine- learning practice and the classical bias–variance trade-off.Proceedings of the National Academy of Sciences, 116(32):15849–15854, July 2019.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Reconciling modern machine- learning practice and the classical bias–variance trade-off.Proceedings of the National Academy of Sciences, 116(32):15849–15854, July 2019

Reference 8

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This paper cites Learning deep architectures for ai.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Learning deep architectures for ai

Reference 9

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Observation 7a367dce-023b-49e0-8bfc-301d37fdac82 · outbound

This paper cites Scaling Learning Algorithms towards AI.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Scaling Learning Algorithms towards AI

Reference 10

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Observation f8b94d34-9176-4b24-85ca-32bdc4d9e6a5 · outbound

This paper cites Greedy layer-wise training of deep networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Greedy layer-wise training of deep networks

Reference 11

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Observation f437dcf3-da5b-4491-ae2a-ae9c67b16592 · outbound

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

Optimizers Qualitatively Alter Solutions And We Should Leverage This Old Optimizer, New Norm: An Anthology

Reference 12

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This paper cites The tradeoffs of large scale learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This The tradeoffs of large scale learning

Reference 13

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This paper cites Large scale online learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Large scale online learning

Reference 14

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Observation cccf5ace-63da-43b2-b3e6-004ac0f53179 · outbound

This paper cites Entropy-SGD: Biasing Gradient Descent Into Wide Valleys.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Entropy-SGD: Biasing Gradient Descent Into Wide Valleys

Reference 15

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Observation 5c1c6b40-1bd1-4546-b316-d76d6c9cba97 · outbound

This paper cites On lazy training in differentiable program- ming.

Optimizers Qualitatively Alter Solutions And We Should Leverage This On lazy training in differentiable program- ming

Reference 16

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Observation 897a9b41-108d-48dd-8398-a46d9495f9e3 · outbound

This paper cites Open problem: The landscape of the loss surfaces of multilayer networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Open problem: The landscape of the loss surfaces of multilayer networks

Reference 17

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Observation 2c8e19f8-6588-489a-bffe-2d40ea1ab63f · outbound

This paper cites Turing completeness of bounded-precision recurrent neural networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Turing completeness of bounded-precision recurrent neural networks

Reference 18

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Observation cb33a1e1-0e8e-4737-b102-94d04b97af92 · outbound

This paper cites Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio

Reference 19

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Observation 2f3d241c-f3e2-4a1f-87b8-a99034d1aba4 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This A continual learning survey: Defying forgetting in classification tasks

Reference 20

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Observation f38362ca-12a6-4277-bd76-3dc258c2e9a9 · outbound

This paper cites Sharp Minima Can Generalize For Deep Nets.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Sharp Minima Can Generalize For Deep Nets

Reference 21

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This paper cites A theoretical analysis of catastrophic forgetting through the ntk overlap matrix.

Optimizers Qualitatively Alter Solutions And We Should Leverage This A theoretical analysis of catastrophic forgetting through the ntk overlap matrix

Reference 22

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This paper cites Continual Backprop: Stochastic Gradient Descent with Persistent Randomness.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 23

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This paper cites Asynchronous Algorithmic Alignment with Cocycles.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Asynchronous Algorithmic Alignment with Cocycles

Reference 24

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Unresolved cited work

Reference 25

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This paper cites Model-agnostic meta-learning for fast adap- tation of deep networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Model-agnostic meta-learning for fast adap- tation of deep networks

Reference 26

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This paper cites Sharpness-aware min- imization for efficiently improving generalization.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Sharpness-aware min- imization for efficiently improving generalization

Reference 27

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Observation 3d8970fe-db91-4f42-9aad-da7d2f3aef40 · outbound

This paper cites Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error

Reference 28

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This paper cites Revisiting "Qualitatively Characterizing Neural Network Optimization Problems".

Optimizers Qualitatively Alter Solutions And We Should Leverage This Revisiting "Qualitatively Characterizing Neural Network Optimization Problems"

Reference 29

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This paper cites Catastrophic forgetting in connectionist networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Catastrophic forgetting in connectionist networks

Reference 30

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This paper cites Understanding the difficulty of training deep feedforward neural networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Understanding the difficulty of training deep feedforward neural networks

Reference 31

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This paper cites Qualitatively characterizing neural network optimization problems.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Qualitatively characterizing neural network optimization problems

Reference 32

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This paper cites Understanding Human Intelligence through Human Limitations.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Understanding Human Intelligence through Human Limitations

Reference 33

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This paper cites Shampoo: Preconditioned Stochastic Tensor Optimization.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Shampoo: Preconditioned Stochastic Tensor Optimization

Reference 34

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Embracing change: Con- tinual learning in deep neural networks

Reference 35

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This paper cites Hinton, Simon Osindero, and Yee-Whye Teh.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Hinton, Simon Osindero, and Yee-Whye Teh

Reference 36

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Flat minima

Reference 37

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Neural tangent kernel: Convergence and generalization in neural networks

Reference 38

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This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Optimizers Qualitatively Alter Solutions And We Should Leverage This On large-batch training for deep learning: Generalization gap and sharp minima

Reference 39

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Overcoming catastrophic forgetting in neural networks

Reference 40

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Unresolved cited work

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Continual learning as computationally constrained reinforcement learning,

Reference 42

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Maintaining plasticity in continual learning via regenerative regularization

Reference 43

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This paper cites Asam: Adaptive sharpness- aware minimization for scale-invariant learning of deep neural networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Asam: Adaptive sharpness- aware minimization for scale-invariant learning of deep neural networks

Reference 44

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This paper cites Directions of Curvature as an Explanation for Loss of Plasticity.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Directions of Curvature as an Explanation for Loss of Plasticity

Reference 45

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Visualizing the loss landscape of neural nets

Reference 46

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Lifelong machine learning: a paradigm for continuous learning

Reference 47

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Decoupled weight decay regularization

Reference 48

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This paper cites Understanding and preventing capacity loss in reinforcement learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Understanding and preventing capacity loss in reinforcement learning

Reference 49

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Normalization and effective learning rates in reinforcement learning

Reference 50

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Optimizers Qualitatively Alter Solutions And We Should Leverage This Deep learning via hessian-free optimization

Reference 51

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

Optimizers Qualitatively Alter Solutions And We Should Leverage This Optimizing neural networks with kronecker-factored approximate curvature

Reference 52

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This paper cites A logical calculus of ideas immanent in nervous activity.

Optimizers Qualitatively Alter Solutions And We Should Leverage This A logical calculus of ideas immanent in nervous activity

Reference 53

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Observation 82978154-a992-458c-a17e-dc9b852ae374 · outbound

This paper cites Minsky and S.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Minsky and S

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.690306Z

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Observation 970ae01c-cc50-475c-bf09-adef742c8997 · outbound

This paper cites Deep Double Descent: Where Bigger Models and More Data Hurt.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.695553Z

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Observation cea9d834-c83b-450e-8b08-1d6be0821226 · outbound

This paper cites Adding Gradient Noise Improves Learning for Very Deep Networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Adding Gradient Noise Improves Learning for Very Deep Networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.702106Z

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Observation 01faa2ae-dc30-4a2b-87ba-cd6e7f7ea44f · outbound

This paper cites Nerem, Samantha Chen, Sanjoy Dasgupta, and Yusu Wang.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Nerem, Samantha Chen, Sanjoy Dasgupta, and Yusu Wang

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:25.453270Z

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Observation e9fd9b8b-fbad-451d-9a91-2826cd7e9c6d · outbound

This paper cites The role of over-parametrization in generalization of neural networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This The role of over-parametrization in generalization of neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:25.287275Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14ad059f-ef90-43f4-9999-33d25112112b · outbound

This paper cites The primacy bias in deep reinforcement learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This The primacy bias in deep reinforcement learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:25.084726Z

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Observation 2af4951e-ab45-44ef-9173-c56ed9f9879d · outbound

This paper cites an unresolved cited work.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:56:24.884761Z

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Observation 8e907d35-ea9a-4bbd-9ef6-088c10674983 · outbound

This paper cites Parisi, Ronald Kemker, Jose L.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Parisi, Ronald Kemker, Jose L

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.724002Z

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Observation 70c4ea2c-accf-4f09-a21d-989d6c64c7ec · outbound

This paper cites On the difficulty of training recurrent neural networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This On the difficulty of training recurrent neural networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:24.683654Z

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Observation ba82417c-4173-43cb-94cc-50a52999137c · outbound

This paper cites Attention is turing-complete.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Attention is turing-complete

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:24.486896Z

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Observation ccc01c0b-7d82-4986-9011-f4fb2b6bae0a · outbound

This paper cites Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.Advances in Neural Information Processing Systems, 36:71095–71134, 2023.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.Advances in Neural Information Processing Systems, 36:71095–71134, 2023

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.736663Z

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Observation 41fefeee-fcff-4ced-afe2-5453345380fe · outbound

This paper cites Sparse feature learning for deep belief networks.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Sparse feature learning for deep belief networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:24.314260Z

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Observation 2abcd8ad-7b84-4540-803b-2e23f22134c1 · outbound

This paper cites Catastrophic forgetting, rehearsal and pseudorehearsal.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Catastrophic forgetting, rehearsal and pseudorehearsal

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:24.150680Z

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Observation d0787047-6e70-4db6-8cb5-1eb6f747225c · outbound

This paper cites Learning representations by back-propagating errors.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Learning representations by back-propagating errors

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.749382Z

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Observation 6d4b1a62-8a4b-4cd1-9609-d6012d52c572 · outbound

This paper cites Powerpropagation: A sparsity inducing weight reparameterisation.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Powerpropagation: A sparsity inducing weight reparameterisation

Reference 68

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T16:56:22.402190Z

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Observation 5d888c67-2052-4fc8-a5d2-9c29096aaf87 · outbound

This paper cites Siegelmann and Eduardo D.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Siegelmann and Eduardo D

Reference 69

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:56:22.315614Z

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Observation 60bd0630-9ce9-4918-898c-aa2c69d96970 · outbound

This paper cites Mas- tering the game of go with deep neural networks and tree search.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Mas- tering the game of go with deep neural networks and tree search

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.761700Z

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Observation 41cc8397-c1ef-4b93-9646-216a1161b522 · outbound

This paper cites WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.

Optimizers Qualitatively Alter Solutions And We Should Leverage This WoodFisher: Efficient Second-Order Approximation for Neural Network Compression

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.765182Z

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Observation 39c4365f-3c07-4803-b417-de3d7d4e2665 · outbound

This paper cites Smith, Benoit Dherin, David G.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Smith, Benoit Dherin, David G

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:23.935503Z

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Observation 10454d02-8d6a-44cb-b5a3-6814fff80c8f · outbound

This paper cites The dormant neuron phenomenon in deep reinforcement learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This The dormant neuron phenomenon in deep reinforcement learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:23.767904Z

Source-reported events for the cited work

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Observation ad3d97d5-c93b-44aa-89c3-64d736760e38 · outbound

This paper cites an unresolved cited work.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Unresolved cited work

Reference 74

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:56:22.072229Z

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Observation de7735b9-1187-464c-bb52-f47bd1ca6156 · outbound

This paper cites Practical issues in temporal difference learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Practical issues in temporal difference learning

Reference 75

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Observation 2bdeb531-d658-48bc-84d6-22d574178a35 · outbound

This paper cites softmax is not enough (for sharp out-of-distribution), 2024.

Optimizers Qualitatively Alter Solutions And We Should Leverage This softmax is not enough (for sharp out-of-distribution), 2024

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:23.620325Z

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Observation 68a370e2-71c8-466f-a177-0ae4efb0e478 · outbound

This paper cites an unresolved cited work.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:56:23.476911Z

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Observation e2fdba17-1994-4f6a-8d86-50d22a5138f6 · outbound

This paper cites Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:23.367835Z

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Observation 9d010b2b-867e-4758-8081-bdf40138134c · outbound

This paper cites Understanding deep learning requires rethinking generalization.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Understanding deep learning requires rethinking generalization

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:23.232897Z

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Observation d7fdcfc5-ba3f-4363-82b7-80dd833a5ddb · outbound

This paper cites doi: 10.1162/neco.1997.9.1.1.

Optimizers Qualitatively Alter Solutions And We Should Leverage This doi: 10.1162/neco.1997.9.1.1

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.618591Z

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Observation c080a08e-0bfc-46c1-a6fa-cf97e8b436c5 · outbound

This paper cites Continual Learning as Computationally Constrained Reinforcement Learning.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Continual Learning as Computationally Constrained Reinforcement Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.643315Z

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Pith citing papers

Observation 125f7d68-abbc-497f-a761-6eddc3f384fd · inbound

Cross-Model Semantics in Representation Learning cites this paper.

Cross-Model Semantics in Representation Learning Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T04:22:15.304977Z

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Observation 558ba87f-680f-4c22-aa79-602ef4f30be9 · inbound

How does the optimizer implicitly bias the model merging loss landscape? cites this paper.

How does the optimizer implicitly bias the model merging loss landscape? Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ef28a47-5dc6-4f83-a96d-f73b047389de · inbound

Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization cites this paper.

Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79f339b7-602b-4362-8404-1d07f0f08213 · inbound

Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws cites this paper.

Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:51:18.269939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0751ef79-8d03-46b6-8692-4a2254da0e66 · inbound

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks cites this paper.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:03:48.001449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e611b25f-9546-4e74-86f1-1ac495debd39 · inbound

Overcoming Rank Collapse in Feedback Alignment cites this paper.

Overcoming Rank Collapse in Feedback Alignment Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:27:37.207966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f9fb4d3-38b5-4b8a-b259-256ee344464b · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:48.677047Z

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