Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws | Paper Citation Record

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This paper cites Sgd learning on neural networks: Leap com- plexity and saddle-to-saddle dynamics.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Sgd learning on neural networks: Leap com- plexity and saddle-to-saddle dynamics

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This paper cites Birkh ¨auser, 2012.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Birkh ¨auser, 2012

Reference 2

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This paper cites Advani, Andrew M.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Advani, Andrew M

Reference 3

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This paper cites Intrinsic dimensionality explains the effectiveness of language model fine-tuning.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Intrinsic dimensionality explains the effectiveness of language model fine-tuning

Reference 4

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This paper cites Implicit regularization in deep matrix factorization.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Implicit regularization in deep matrix factorization

Reference 5

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This paper cites Max-margin token selection in attention mechanism.Advances in neural information processing systems, 36:48314–48362, 2023.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Max-margin token selection in attention mechanism.Advances in neural information processing systems, 36:48314–48362, 2023

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This paper cites Explaining neural scaling laws.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Explaining neural scaling laws

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This paper cites Statistical mechanics of deep learning.Annual Review of Condensed Matter Physics, 11:501–528, 2020.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Statistical mechanics of deep learning.Annual Review of Condensed Matter Physics, 11:501–528, 2020

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This paper cites VICReg: Variance-invariance-covariance regularization for self- supervised learning.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws VICReg: Variance-invariance-covariance regularization for self- supervised learning

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This paper cites Mechanism of feature learning in convolutional neural networks.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Mechanism of feature learning in convolutional neural networks

Reference 10

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This paper cites Erdogdu, Nuri Mert Vural, and Denny Wu.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Erdogdu, Nuri Mert Vural, and Denny Wu

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This paper cites Incremental learning in diagonal linear networks.Journal of Machine Learning Research, 24(171):1–26, 2023.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Incremental learning in diagonal linear networks.Journal of Machine Learning Research, 24(171):1–26, 2023

Reference 12

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This paper cites Single-head attention in high dimensions: A theory of generalization, weights spectra, and scaling laws.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Single-head attention in high dimensions: A theory of generalization, weights spectra, and scaling laws

Reference 13

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This paper cites A dynamical model of neural scaling laws.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws A dynamical model of neural scaling laws

Reference 14

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This paper cites Spectrum dependent learning curves in kernel re- gression and wide neural networks.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Spectrum dependent learning curves in kernel re- gression and wide neural networks

Reference 15

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This paper cites Ecological communities with Lotka–Volterra dynamics.Phys.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Ecological communities with Lotka–Volterra dynamics.Phys

Reference 16

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This paper cites Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks.Nature Communications, 12(2914), 2021.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks.Nature Communications, 12(2914), 2021

Reference 17

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This paper cites Cand `es, Xiaodong Li, and Mahdi Soltanolkotabi.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Cand `es, Xiaodong Li, and Mahdi Soltanolkotabi

Reference 18

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This paper cites Tight sample complexity of learning one-hidden-layer convolutional neural net- works.Advances in Neural Information Processing Systems, 32, 2019.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Tight sample complexity of learning one-hidden-layer convolutional neural net- works.Advances in Neural Information Processing Systems, 32, 2019

Reference 19

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This paper cites Machine learning and the physical sciences.Rev.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Machine learning and the physical sciences.Rev

Reference 20

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This paper cites Chaikin and Tom C.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Chaikin and Tom C

Reference 21

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This paper cites On lazy training in differentiable programming.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws On lazy training in differentiable programming

Reference 22

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This paper cites Scaling laws and spectra of shallow neural networks in the feature learning regime.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Scaling laws and spectra of shallow neural networks in the feature learning regime

Reference 23

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This paper cites Gradient descent learns one-hidden- layer cnn: Don’t be afraid of spurious local minima.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Gradient descent learns one-hidden- layer cnn: Don’t be afraid of spurious local minima

Reference 24

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This paper cites Cambridge University Press, Cambridge, 2001.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Cambridge University Press, Cambridge, 2001

Reference 25

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This paper cites Bilinear sequence regression: A model for learning from long sequences of high-dimensional tokens.Physical Review X, 15(2):021092, 2025.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Bilinear sequence regression: A model for learning from long sequences of high-dimensional tokens.Physical Review X, 15(2):021092, 2025

Reference 26

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This paper cites (S)GD over diagonal linear networks: Implicit bias, large stepsizes and edge of stability.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws (S)GD over diagonal linear networks: Implicit bias, large stepsizes and edge of stability

Reference 27

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 28

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This paper cites A regularity condition of the information matrix of a multilayer perceptron network.Neural Networks, 9(5):871–879, 1996.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws A regularity condition of the information matrix of a multilayer perceptron network.Neural Networks, 9(5):871–879, 1996

Reference 29

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This paper cites Matrix completion has no spurious local minimum.Advances in neural information processing systems, 29, 2016.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Matrix completion has no spurious local minimum.Advances in neural information processing systems, 29, 2016

Reference 30

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This paper cites word2vec explained: Deriving mikolov et al.’s negative-sampling word- embedding method, 2014.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws word2vec explained: Deriving mikolov et al.’s negative-sampling word- embedding method, 2014

Reference 31

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This paper cites Addison-Wesley, Reading, MA, 1992.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Addison-Wesley, Reading, MA, 1992

Reference 32

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This paper cites Implicit regularization in matrix factorization.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Implicit regularization in matrix factorization

Reference 33

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This paper cites Gradient Descent Happens in a Tiny Subspace.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Gradient Descent Happens in a Tiny Subspace

Reference 34

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This paper cites Cambridge university press, 1998.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Cambridge university press, 1998

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This paper cites Rae, Oriol Vinyals, and Laurent Sifre.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Rae, Oriol Vinyals, and Laurent Sifre

Reference 36

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This paper cites Position: The platonic representation hypoth- esis.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Position: The platonic representation hypoth- esis

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Neural tangent kernel: Convergence and generalization in neural networks

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This paper cites Scaling Laws for Neural Language Models.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Scaling Laws for Neural Language Models

Reference 39

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This paper cites The universal weight subspace hypothesis, 2025.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws The universal weight subspace hypothesis, 2025

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This paper cites Matrix factorization techniques for recommender systems.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Matrix factorization techniques for recommender systems

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This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

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This paper cites Alternating gradient flows: A theory of feature learning in two-layer neural net- works.Advances in Neural Information Processing Systems, 38:4377–4424, 2025.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Alternating gradient flows: A theory of feature learning in two-layer neural net- works.Advances in Neural Information Processing Systems, 38:4377–4424, 2025

Reference 43

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This paper cites Elsevier, 2013.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Elsevier, 2013

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Observation 5d3ba122-e722-4d14-9303-adf7e57e4f74 · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Measuring the intrinsic dimension of objective landscapes

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Observation 8f0e7053-36bf-4026-ab2d-7c91723fc789 · outbound

This paper cites Towards under- standing grokking: An effective theory of representation learning.Advances in Neural Information Processing Systems, 35:34651–34663, 2022.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Towards under- standing grokking: An effective theory of representation learning.Advances in Neural Information Processing Systems, 35:34651–34663, 2022

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.930198Z

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Observation b3592998-26bd-4486-8f4f-ac651f5b491d · outbound

This paper cites Phase retrieval in high dimensions: Statistical and computational phase transitions.Advances in Neural Information Processing Systems, 33:11071– 11082, 2020.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Phase retrieval in high dimensions: Statistical and computational phase transitions.Advances in Neural Information Processing Systems, 33:11071– 11082, 2020

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.910819Z

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Observation 2ec0829b-b46f-4c6b-9c9e-7eebbf4e0deb · outbound

This paper cites Bayes-optimal learning of an extensive-width neural network from quadratically many samples.Advances in Neural Information Processing Systems, 37:82085–82132, 2024.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Bayes-optimal learning of an extensive-width neural network from quadratically many samples.Advances in Neural Information Processing Systems, 37:82085–82132, 2024

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This paper cites A Solvable Model of Neural Scaling Laws.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws A Solvable Model of Neural Scaling Laws

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This paper cites Attention-based clustering.Advances in Neural Infor- mation Processing Systems, 38:66455–66506, 2025.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Attention-based clustering.Advances in Neural Infor- mation Processing Systems, 38:66455–66506, 2025

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Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 51

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 85ae381b-6422-4f60-b54c-88e7c8a9814a · outbound

This paper cites Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.769501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d51ae73-c348-44ab-996b-e79f652aaeac · outbound

This paper cites A defense of the quadratic model, 2026.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws A defense of the quadratic model, 2026

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.828033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 518e8e3e-a9d0-4ba4-b260-59a3260f0f18 · outbound

This paper cites The quantization model of neural scaling.Advances in Neural Information Processing Systems, 36:28699–28722, 2023.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws The quantization model of neural scaling.Advances in Neural Information Processing Systems, 36:28699–28722, 2023

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.780443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb72e1c4-d548-4397-8b13-5c8d4128c1d6 · outbound

This paper cites Corrado, and Jeff Dean.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Corrado, and Jeff Dean

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.798952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79b1f8ad-fcad-4357-9e9a-495c29e06a6b · outbound

This paper cites An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem.Advances in Neural Information Processing Systems, 37:39632–39693, 2024.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem.Advances in Neural Information Processing Systems, 37:39632–39693, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.781838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2f7137cb-caa5-4837-af3e-9460b8293113 · outbound

This paper cites Sigmoid gating is more sample efficient than softmax gating in mixture of experts.Advances in Neural Information Processing Systems, 37:118357–118388, 2024.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Sigmoid gating is more sample efficient than softmax gating in mixture of experts.Advances in Neural Information Processing Systems, 37:118357–118388, 2024

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.764232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 88cf6706-61f1-4eb6-a5dd-d071307fceaa · outbound

This paper cites Dissecting query-key interaction in vision transformers.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Dissecting query-key interaction in vision transformers

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.746607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0f2a9eee-82b5-4f53-90dc-2739676f4b92 · outbound

This paper cites Implicit bias of sgd for diagonal linear networks: a provable benefit of stochasticity.Advances in Neural Information Processing Systems, 34:29218–29230, 2021.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Implicit bias of sgd for diagonal linear networks: a provable benefit of stochasticity.Advances in Neural Information Processing Systems, 34:29218–29230, 2021

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.804005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87acbc1d-57a7-475f-af03-98d452af7d00 · outbound

This paper cites Pope.Turbulent Flows.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Pope.Turbulent Flows

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.810028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f26f3966-f316-4ac8-81aa-c0c3f1c62cc4 · outbound

This paper cites Mechanism for feature learning in neural networks and backpropagation-free machine learning models.Science, 383(6690):1461–1467, 2024.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Mechanism for feature learning in neural networks and backpropagation-free machine learning models.Science, 383(6690):1461–1467, 2024

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.814958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9216362-865b-4882-9a5c-64af59a16459 · outbound

This paper cites Saxe, James L.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Saxe, James L

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.821373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9298fbf6-0223-4147-9070-2433024ba279 · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.677953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e034e4a-4e10-4864-b013-f79e8822b754 · outbound

This paper cites Le, Geoffrey E.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Le, Geoffrey E

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.659618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95c436d8-7b6c-4c1f-b8d1-47ae983c090b · outbound

This paper cites Maximum-margin matrix factorization.Advances in neural information processing systems, 17, 2004.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Maximum-margin matrix factorization.Advances in neural information processing systems, 17, 2004

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.640895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f7f91fe0-226c-4d99-9634-504c8116cbd3 · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.620161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ee3ba760-3827-4297-b8be-cd8956ba48ea · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:46.845816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c2602e-78b8-4891-bae3-ab4d98eafc64 · outbound

This paper cites Self-supervised learning with data augmentations provably isolates content from style.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Self-supervised learning with data augmentations provably isolates content from style

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.587758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dae01301-c4a6-4387-b10c-d1b98e981403 · outbound

This paper cites A universal compression theory for lottery ticket hypothesis and neural scaling laws.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws A universal compression theory for lottery ticket hypothesis and neural scaling laws

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.569765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba7f8bb1-b6b5-4d47-b251-0b95ee6bbb08 · outbound

This paper cites Lee, and Denny Wu.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Lee, and Denny Wu

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.551447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e1218d0-d1c7-40cd-b291-e5231229e240 · outbound

This paper cites Chuang, and Max Tegmark.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Chuang, and Max Tegmark

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.529340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6295dd50-b7ab-49a1-ab6c-324a97da691e · outbound

This paper cites Fundamental limits of matrix sensing: Exact asymptotics, universality, and applications.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Fundamental limits of matrix sensing: Exact asymptotics, universality, and applications

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.511687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 00244f77-beb0-4ca0-a0bf-7138f9047f76 · outbound

This paper cites Three mechanisms of feature learning in a linear network.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Three mechanisms of feature learning in a linear network

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.493951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7cb77dce-23fe-42e1-adcd-9eedd7471329 · outbound

This paper cites Statistical physics of inference: Thresholds and algorithms.Advances in Physics, 65(5):453–552, 2016.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Statistical physics of inference: Thresholds and algorithms.Advances in Physics, 65(5):453–552, 2016

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.473826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3a106f6a-adc6-42b0-ad40-6ac0466fbdc2 · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.452066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation daba1b73-f9eb-4de6-b0ac-06305317d731 · outbound

This paper cites Quadratic models for under- standing catapult dynamics of neural networks.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Quadratic models for under- standing catapult dynamics of neural networks

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.433858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 619db255-f9f9-4719-8f40-3fc3003c2812 · outbound

This paper cites Symmetry induces structure and constraint of learning.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Symmetry induces structure and constraint of learning

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.417739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3b20ae60-82b0-4887-a91a-bcb3fc2151dc · outbound

This paper cites What shapes the loss landscape of self-supervised learning? InInternational Conference on Learning Representations, 2023.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws What shapes the loss landscape of self-supervised learning? InInternational Conference on Learning Representations, 2023

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.393707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 880a800f-6698-4b09-8c54-769950285871 · outbound

This paper cites Parameter symmetry and noise equilibrium of stochastic gradient descent.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Parameter symmetry and noise equilibrium of stochastic gradient descent

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.371028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 02a83b15-4ebb-4e9d-9d17-da785974f7d4 · outbound

This paper cites Parameter symmetry potentially unifies deep learning theory, 2025.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Parameter symmetry potentially unifies deep learning theory, 2025

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.352803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ef31d314-2240-4e2f-a8a5-7e7528fc0190 · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.332736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 55a75f72-3c69-4fe3-bcf6-7472572264d6 · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.311041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9609027d-51dc-4924-8c9d-3f6946737db9 · outbound

This paper cites Finally let us computeA(x)= 1 2 Hii, whereH ii =∇ 2 wi fx∣wi=0 is the Hessian matrix with respect to thei-th neuron’s parametersw i =[u ⊺ i , vi]⊺.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Finally let us computeA(x)= 1 2 Hii, whereH ii =∇ 2 wi fx∣wi=0 is the Hessian matrix with respect to thei-th neuron’s parametersw i =[u ⊺ i , vi]⊺

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.288931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba890a07-12ff-4947-b550-f234a99a9641 · outbound

This paper cites Thus,f x isC ∞.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Thus,f x isC ∞

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.269977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1bb5723d-307d-41ce-b125-6f2a78df2b9b · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.246068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20c9bab8-1680-4b13-a2ef-356549391314 · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 86

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T13:30:47.225133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4ffaa7f3-0d88-4264-9b49-db3591cecbae · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.201818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 842eb695-7510-41a6-9f66-c3377c04c62f · outbound

This paper cites an unresolved cited work.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:47.183648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 47778649-e4d9-4d95-88e0-759294b60761 · outbound

This paper cites The NTK remains invariant while the loss decreases byO(1).

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws The NTK remains invariant while the loss decreases byO(1)

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.164640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 12156a9d-3efd-436b-a213-ebe413c9b689 · outbound

This paper cites The NTK changes on the same timescale as the loss, allowing the model to learn data-dependent representations.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws The NTK changes on the same timescale as the loss, allowing the model to learn data-dependent representations

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.142203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d814d490-cccc-40a2-8082-0fb966dbb936 · outbound

This paper cites Sinceα g =1/2andα B ≥1/2, we haveu=g+2Bµ=O(d −1/2).

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Sinceα g =1/2andα B ≥1/2, we haveu=g+2Bµ=O(d −1/2)

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.121094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44a2efe3-9925-4a8f-9f27-b322e5ca8a1f · outbound

This paper cites Momentum.

Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Momentum

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:47.100999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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