Paper Citation Record · LEDGER

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers

As of 23 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 2 inbound Pith citation observations for arXiv:2505.15239.

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

pith.paper-citation-record.v1
2505.15239 v1

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measured 76 of 76 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

76 of 76 outbound references displayed

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External citation measurements

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Outbound references

Observation 9846464d-742d-4d8e-b572-876656df8420 · outbound

This paper cites The prevalence of neural collapse in neural multivariate regression.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers The prevalence of neural collapse in neural multivariate regression

Reference 1

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This paper cites Intrinsic dimension of data representa- tions in deep neural networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Intrinsic dimension of data representa- tions in deep neural networks

Reference 2

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Observation 2dd94fbe-02ae-4e18-b570-83b3d498e3a3 · outbound

This paper cites Average gradient outer product as a mechanism for deep neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Average gradient outer product as a mechanism for deep neural collapse

Reference 3

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Observation 9620f95c-5bdf-4ab2-9d11-a3478b435982 · outbound

This paper cites On the inductive bias of infinite-depth ResNets and the bottleneck rank.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers On the inductive bias of infinite-depth ResNets and the bottleneck rank

Reference 4

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This paper cites Prevalence of simplex compression in adversarial deep neural networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Prevalence of simplex compression in adversarial deep neural networks

Reference 5

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Observation 2a3bc718-4c7b-4cf3-9e52-d922c78dd334 · outbound

This paper cites Perfectly balanced: Improving transfer and robustness of supervised contrastive learning.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Perfectly balanced: Improving transfer and robustness of supervised contrastive learning

Reference 6

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This paper cites Neural collapse for cross-entropy class-imbalanced learning with unconstrained relu features model.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural collapse for cross-entropy class-imbalanced learning with unconstrained relu features model

Reference 7

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This paper cites Neural collapse in deep linear network: From balanced to imbalanced data.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural collapse in deep linear network: From balanced to imbalanced data

Reference 8

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This paper cites Improving self-supervised learning by characterizing idealized representations.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Improving self-supervised learning by characterizing idealized representations

Reference 9

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Observation 7cdb0dad-37b9-492e-a2bb-97d8d73f8b59 · outbound

This paper cites Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training

Reference 10

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Observation 5e8a58ee-250e-42b0-a617-bf6aed21d6be · outbound

This paper cites On the Implicit Bias Towards Minimal Depth of Deep Neural Networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers On the Implicit Bias Towards Minimal Depth of Deep Neural Networks

Reference 11

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This paper cites Improved generalization bounds for transfer learning via neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Improved generalization bounds for transfer learning via neural collapse

Reference 12

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Observation dfaa063c-7b16-4752-aa7d-a13bf43e6b92 · outbound

This paper cites The persistence of neural collapse despite low-rank bias: An analytic perspective through unconstrained features.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers The persistence of neural collapse despite low-rank bias: An analytic perspective through unconstrained features

Reference 13

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This paper cites Unifying Low Dimensional Spectra in Deep Learning.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unifying Low Dimensional Spectra in Deep Learning

Reference 14

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This paper cites Linking neural collapse and l2 normalization with improved out-of-distribution detection in deep neural networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Linking neural collapse and l2 normalization with improved out-of-distribution detection in deep neural networks

Reference 15

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

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This paper cites A law of data separation in deep learning.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers A law of data separation in deep learning

Reference 17

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This paper cites Deep residual learning for image recognition.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Deep residual learning for image recognition

Reference 18

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This paper cites Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data

Reference 19

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This paper cites Neural collapse for unconstrained feature model under cross-entropy loss with imbalanced data.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural collapse for unconstrained feature model under cross-entropy loss with imbalanced data

Reference 20

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

Reference 21

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This paper cites Limitations of Neural Collapse for Understanding Generalization in Deep Learning.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Limitations of Neural Collapse for Understanding Generalization in Deep Learning

Reference 22

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This paper cites Wide neural networks trained with weight decay provably exhibit neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Wide neural networks trained with weight decay provably exhibit neural collapse

Reference 23

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This paper cites An unconstrained layer-peeled perspective on neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers An unconstrained layer-peeled perspective on neural collapse

Reference 24

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This paper cites Generalized neural collapse for a large number of classes.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Generalized neural collapse for a large number of classes

Reference 25

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This paper cites How does information bottleneck help deep learning? In International Conference on Machine Learning (ICML), 2023.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers How does information bottleneck help deep learning? In International Conference on Machine Learning (ICML), 2023

Reference 26

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Kernel vs

Reference 27

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This paper cites A neural collapse perspective on feature evolution in graph neural networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers A neural collapse perspective on feature evolution in graph neural networks

Reference 28

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This paper cites Learning multiple layers of features from tiny images.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Learning multiple layers of features from tiny images

Reference 29

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This paper cites The asymmetric maximum margin bias of quasi-homogeneous neural networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers The asymmetric maximum margin bias of quasi-homogeneous neural networks

Reference 30

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This paper cites Gradient-based learning applied to document recognition.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Gradient-based learning applied to document recognition

Reference 31

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This paper cites Principled and efficient transfer learning of deep models via neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Principled and efficient transfer learning of deep models via neural collapse

Reference 32

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This paper cites No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier

Reference 33

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Inducing neural collapse to a fixed hierarchy-aware frame for reducing mistake severity

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Spurious feature diversification improves out-of-distribution generalization

Reference 35

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This paper cites The Exploration of Neural Collapse under Imbalanced Data.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers The Exploration of Neural Collapse under Imbalanced Data

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Gen: Pushing the limits of softmax-based out-of-distribution detection

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This paper cites Inducing neural collapse in deep long-tailed learning.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Inducing neural collapse in deep long-tailed learning

Reference 38

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural collapse under cross-entropy loss

Reference 39

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This paper cites Do we need neural collapse? Learning diverse features for fine-grained and long-tail classification.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Do we need neural collapse? Learning diverse features for fine-grained and long-tail classification

Reference 40

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This paper cites The tunnel effect: Building data representations in deep neural networks.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers The tunnel effect: Building data representations in deep neural networks

Reference 41

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This paper cites Neural collapse with unconstrained features.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural collapse with unconstrained features

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

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This paper cites Neural collapse in deep homogeneous classifiers and the role of weight decay.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural collapse in deep homogeneous classifiers and the role of weight decay

Reference 44

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This paper cites Feature learning in deep classifiers through intermediate neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Feature learning in deep classifiers through intermediate neural collapse

Reference 45

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This paper cites Neural (tangent kernel) collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural (tangent kernel) collapse

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This paper cites On the robustness of neural collapse and the neural collapse of robustness.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers On the robustness of neural collapse and the neural collapse of robustness

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Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

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This paper cites Implicit optimization bias of next-token prediction in linear models.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Implicit optimization bias of next-token prediction in linear models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:40.938642Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ef041ce-71ba-4135-9215-a139eff8728e · outbound

This paper cites Imbalance trouble: Revisiting neural-collapse geometry.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Imbalance trouble: Revisiting neural-collapse geometry

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:40.699177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8653096-01da-4baa-9bae-646bea903968 · outbound

This paper cites Extended unconstrained features model for exploring deep neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Extended unconstrained features model for exploring deep neural collapse

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:40.548188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 75e278c2-258f-4911-b8e7-aa0410bfdecd · outbound

This paper cites Perturbation analysis of neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Perturbation analysis of neural collapse

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:40.349583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 377d1d83-26d4-4e1b-a30a-1b30163bfd01 · outbound

This paper cites Attention is all you need.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Attention is all you need

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:34.510160Z

Source-reported events for the cited work

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Observation 03bd2be7-16ee-4a95-bf72-0d35aeb561cb · outbound

This paper cites Get the best of both worlds: Improving accuracy and transferability by grassmann class representation.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Get the best of both worlds: Improving accuracy and transferability by grassmann class representation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:40.073635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ed2e6243-381c-4482-b988-0fa2f881d8a1 · outbound

This paper cites Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:34.702471Z

Source-reported events for the cited work

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Observation 269429fd-5355-45ed-9f28-a1be021c3394 · outbound

This paper cites Linear convergence analysis of neural collapse with unconstrained features.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Linear convergence analysis of neural collapse with unconstrained features

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:39.874408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d828600a-71d1-4028-bf96-2250adf96c96 · outbound

This paper cites Progressive Feedforward Collapse of ResNet Training.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Progressive Feedforward Collapse of ResNet Training

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:34.877392Z

Source-reported events for the cited work

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Observation 90cf3d23-7bd5-4766-a60e-46c694258d50 · outbound

This paper cites How far pre-trained models are from neural collapse on the target dataset informs their transferability.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers How far pre-trained models are from neural collapse on the target dataset informs their transferability

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:39.583346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6ddbabf0-bac5-40ee-a7e1-46c79286e668 · outbound

This paper cites On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:39.423883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ab79a979-049e-4d91-bd42-7ffbd27e53ec · outbound

This paper cites Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:35.159012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44c17b53-b122-4db6-8b1c-870ed91df133 · outbound

This paper cites Linguistic Collapse: Neural Collapse in (Large) Language Models.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Linguistic Collapse: Neural Collapse in (Large) Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:35.247658Z

Source-reported events for the cited work

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Observation 87e81fca-547a-484c-ab84-06e72231df45 · outbound

This paper cites Pursuing feature separation based on neural collapse for out-of-distribution detection.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Pursuing feature separation based on neural collapse for out-of-distribution detection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:39.167053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f226edc2-3386-4730-98b8-b4c168e48e36 · outbound

This paper cites Dynamics in deep classifiers trained with the square loss: Normalization, low rank, neural collapse, and generalization bounds.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Dynamics in deep classifiers trained with the square loss: Normalization, low rank, neural collapse, and generalization bounds

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:38.959030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0d0c3110-66ce-4448-942d-1e106dd0334c · outbound

This paper cites Epa: Neural collapse inspired robust out-of- distribution detector.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Epa: Neural collapse inspired robust out-of- distribution detector

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:38.762058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 66c9f432-3e04-4d93-8cd4-95aab63eeee9 · outbound

This paper cites Implicit geometry of next-token prediction: From language sparsity patterns to model representations.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Implicit geometry of next-token prediction: From language sparsity patterns to model representations

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:38.608768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9887efea-1e49-4162-9dfe-588245d8425f · outbound

This paper cites Understand- ing imbalanced semantic segmentation through neural collapse.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Understand- ing imbalanced semantic segmentation through neural collapse

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:38.447192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c61d8ba6-55f6-40fd-9d88-df00a533ecd0 · outbound

This paper cites On the optimization landscape of neural collapse under MSE loss: Global optimality with unconstrained features.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers On the optimization landscape of neural collapse under MSE loss: Global optimality with unconstrained features

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:38.227964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79accd6d-8faa-4f16-b31e-6d35dd780fef · outbound

This paper cites Are all losses created equal: A neural collapse perspective.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Are all losses created equal: A neural collapse perspective

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:38.032659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8c71310e-3e41-489b-b6bd-0bf272ab0da8 · outbound

This paper cites Balanced contrastive learning for long-tailed visual recognition.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Balanced contrastive learning for long-tailed visual recognition

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:37.888997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2e013ca7-6580-4f40-bba9-14319a1a5f86 · outbound

This paper cites A geometric analysis of neural collapse with unconstrained features.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers A geometric analysis of neural collapse with unconstrained features

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:37.714048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5894bf0-263a-452b-8fe5-f9ae6ed08186 · outbound

This paper cites an unresolved cited work.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:37.507633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5b281216-730f-4172-afb7-a4fa83dd585a · outbound

This paper cites an unresolved cited work.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:37.357653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b93ca3a1-4317-4bfa-95d0-92fb2ae6fb6a · outbound

This paper cites Denote ¯Gki as the set of points on Gki between xki and ¯xki.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Denote ¯Gki as the set of points on Gki between xki and ¯xki

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:37.217865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 504d3e5c-e801-4baf-8d1a-10c881083362 · outbound

This paper cites an unresolved cited work.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:37.032452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa7dd894-af75-4b6e-a532-d941f3a1f824 · outbound

This paper cites 10cm ≤ (d − max j(ki)̸=j(lp) ¯hT j(ki) ¯hj(lp))/d.

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers 10cm ≤ (d − max j(ki)̸=j(lp) ¯hT j(ki) ¯hj(lp))/d

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:36.864014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 78cb20d8-3a16-480d-ae28-4153551ad9f7 · inbound

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension cites this paper.

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:22:33.217125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ec783da-1013-460e-92c7-5ee46817e75b · inbound

Structure Before Collapse: Transient semantic geometry in next-token prediction cites this paper.

Structure Before Collapse: Transient semantic geometry in next-token prediction Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:29:50.957179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.