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Paper Citation Record · LEDGER

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias

As of 7 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2506.06280.

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

pith.paper-citation-record.v1
2506.06280 v2

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:51.972799Z

measured 75 of 75 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:12:34.296158Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:12:37.393299Z

Reference resolution

74 of 74 outbound references displayed

  • verified exact3
  • verified fuzzy28
  • unresolved43
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 732be365-4dff-4add-b42d-9882b4c2aa93 · outbound

This paper cites The neural tangent kernel in high dimensions: Triple descent and a multi-scale theory of generalization.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias The neural tangent kernel in high dimensions: Triple descent and a multi-scale theory of generalization

Reference 1

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Source-reported events for the cited work

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Observation 5b588b8a-d488-43f3-81ba-2ffe372a377b · outbound

This paper cites powerlaw: a python package for analysis of heavy-tailed distributions.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias powerlaw: a python package for analysis of heavy-tailed distributions

Reference 2

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Source-reported events for the cited work

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Observation 4233dee0-345f-4edf-b194-10c49200eb12 · outbound

This paper cites High-dimensional asymptotics of feature learning: How one gradient step improves the representation.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias High-dimensional asymptotics of feature learning: How one gradient step improves the representation

Reference 3

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Observation d2b71af2-15fa-4273-bf6a-c0b305124183 · outbound

This paper cites Spectral analysis of large dimensional random matrices, volume 20.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Spectral analysis of large dimensional random matrices, volume 20

Reference 4

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Source-reported events for the cited work

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Observation 079aa85f-37d3-42bc-9805-d65cb46ea714 · outbound

This paper cites Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f8a57f33-5050-4002-a3cc-a5801f2450ed · outbound

This paper cites Policy learning from tutorial books via understanding, rehearsing and introspecting.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Policy learning from tutorial books via understanding, rehearsing and introspecting

Reference 6

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Source-reported events for the cited work

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Observation 4c1a2e59-b3a3-4706-97d9-24f7df74bdcf · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 7

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Source-reported events for the cited work

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Observation cd9ffd53-d7fd-4819-b0e1-7d813db58903 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

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Observation 57227f9d-6a41-4752-b620-55096ec2bce5 · outbound

This paper cites Power-law distributions in empirical data.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Power-law distributions in empirical data

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 36517b75-37e1-4bbd-925c-c0bad721a8d5 · outbound

This paper cites Random Matrix Methods for Machine Learning.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Random Matrix Methods for Machine Learning

Reference 10

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Source-reported events for the cited work

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Observation 139422d4-0e27-4663-9c77-066691da0a59 · outbound

This paper cites Exact expressions for double descent and implicit regularization via surrogate random design.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Exact expressions for double descent and implicit regularization via surrogate random design

Reference 11

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Source-reported events for the cited work

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Observation b842158a-6bde-47b9-8cd3-2d033c7022a5 · outbound

This paper cites High-dimensional asymptotics of prediction: Ridge regression and classification.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias High-dimensional asymptotics of prediction: Ridge regression and classification

Reference 12

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Source-reported events for the cited work

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Observation aaa82bb2-317c-4990-a6b0-dc45bc686ff6 · outbound

This paper cites Generalizable Adversarial Training via Spectral Normalization.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Generalizable Adversarial Training via Spectral Normalization

Reference 13

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Observation c8a3653e-7d44-4211-835d-4868a2301122 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 14

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Source-reported events for the cited work

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Observation 516cb8c4-ef5f-40fa-a83d-dba3a8275186 · outbound

This paper cites A framework for few-shot language model evaluation.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias A framework for few-shot language model evaluation

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5cc94017-8631-4d1d-810c-50bfb72c55d5 · outbound

This paper cites The Llama 3 Herd of Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias The Llama 3 Herd of Models

Reference 16

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Observation be6a616a-fca2-4fbe-9141-285b976a2f4d · outbound

This paper cites Weidenmüller.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Weidenmüller

Reference 17

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Source-reported events for the cited work

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Observation 5f29d374-3906-4217-92b8-7cf47a2434b4 · outbound

This paper cites The heavy-tail phenomenon in sgd.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias The heavy-tail phenomenon in sgd

Reference 18

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Source-reported events for the cited work

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Observation b5f5e7f4-d5ae-4c49-900c-91b7367f2379 · outbound

This paper cites Learning both weights and connections for efficient neural network.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Learning both weights and connections for efficient neural network

Reference 19

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Source-reported events for the cited work

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Observation c793f809-80b1-4c22-8434-65beb61ac277 · outbound

This paper cites DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training

Reference 20

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Observation 00bb0077-7dc3-4947-ae3b-6fa3c6953f3a · outbound

This paper cites Surprises in high-dimensional ridgeless least squares interpolation.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Surprises in high-dimensional ridgeless least squares interpolation

Reference 21

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Observation 199ef913-5abb-4be3-a64a-ba70b326263b · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 22

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Observation ff2bf95b-9f60-47c0-8bbe-4d55ad69f60a · outbound

This paper cites Deep residual learning for image recognition.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Deep residual learning for image recognition

Reference 23

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Observation 9beb227d-8103-442c-b5da-45de1b9d0955 · outbound

This paper cites A simple general approach to inference about the tail of a distribution.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias A simple general approach to inference about the tail of a distribution

Reference 24

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Source-reported events for the cited work

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Observation 2a5f182c-16d6-4f17-9a83-ac3d183faab2 · outbound

This paper cites Multiplicative noise and heavy tails in stochastic optimization.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Multiplicative noise and heavy tails in stochastic optimization

Reference 25

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Source-reported events for the cited work

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Observation 9e18fbb4-472c-4c7b-a262-030bd38dfa49 · outbound

This paper cites Generalization bounds using lower tail exponents in stochastic optimizers.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Generalization bounds using lower tail exponents in stochastic optimizers

Reference 26

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Observation 845f4b70-fd76-4090-847e-6c713ff9d976 · outbound

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Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Universality laws for high-dimensional learning with random features

Reference 27

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Source-reported events for the cited work

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Observation 8dc60cf9-907f-4387-b44f-1c1320169106 · outbound

This paper cites From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks

Reference 28

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Source-reported events for the cited work

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Observation 68cb168f-7242-4399-ac8d-acbf1c5368c4 · outbound

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

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Learning multiple layers of features from tiny images

Reference 29

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Observation deeced84-2447-45c9-9695-85d85c712414 · outbound

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Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias BaWA : Automatic optimizing pruning metric for large language models with balanced weight and activation

Reference 30

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Source-reported events for the cited work

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Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Model Balancing Helps Low-data Training and Fine-tuning

Reference 31

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Source-reported events for the cited work

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This paper cites Lift the veil for the truth: Principal weights emerge after rank reduction for reasoning-focused supervised fine-tuning.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Lift the veil for the truth: Principal weights emerge after rank reduction for reasoning-focused supervised fine-tuning

Reference 32

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Source-reported events for the cited work

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Observation ea833ff8-10f9-4fb1-90b2-905f69a19ac9 · outbound

This paper cites AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 33

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Observation 62bfd72d-bddc-4a8c-8c7c-12af1970c10c · outbound

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Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Traditional and heavy tailed self regularization in neural network models

Reference 34

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Source-reported events for the cited work

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Observation 0d8389a0-9de5-4f32-9237-be8fd637dcb0 · outbound

This paper cites Traditional and Heavy-Tailed Self Regularization in Neural Network Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Traditional and Heavy-Tailed Self Regularization in Neural Network Models

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 626276c1-0762-4181-958d-aa4aefe79aec · outbound

This paper cites Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning

Reference 36

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raw_fallback, observed 2026-08-07T06:00:52.734471Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.823349Z digest=sha256:7fe1eef7e0fc5e887d47cccd15ce3fbc099e7def66fbc411d9b2ede3bdeefbe3

Observation 8d4de6ec-ac16-4ad6-b8fb-2e982a794a35 · outbound

This paper cites Martin, Tongsu Peng, and Michael W.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Martin, Tongsu Peng, and Michael W

Reference 37

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no resolver link, observed 2026-08-07T06:00:51.826610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.826610Z digest=sha256:078787e76651a1932ee9982d424ac69c6d105700ee3ecf7418191f35c45cdd20

Observation ee2ba575-99ff-4eb0-bb52-a6a9e8012a83 · outbound

This paper cites The generalization error of random features regression: Precise asymptotics and the double descent curve.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias The generalization error of random features regression: Precise asymptotics and the double descent curve

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.721782Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.830605Z digest=sha256:6188787a1dc215d0394c81c8a052332279b1ec5678a3d39f4c7ad97884011553

Observation e76c3c1a-d9ec-4ccd-b069-e83f7c537ecf · outbound

This paper cites Pointer Sentinel Mixture Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Pointer Sentinel Mixture Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.834169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.834169Z digest=sha256:9e090dc23b305e3a6f40f97db8b57dbd9b7d609cb8f7d3afeeaf08ab94bfb276

Observation e1bc72a5-4fc6-431f-b052-15ef7478c07d · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 40

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unresolved
no resolver link, observed 2026-08-07T06:00:51.837865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.837865Z digest=sha256:4cd093d185fd9a9d431c007decef47698a689323c360029392fb851b1343cc76

Observation adf9ab93-26ef-4299-a41c-a761b1d1ec43 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Spectral Normalization for Generative Adversarial Networks

Reference 41

Resolution
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no resolver link, observed 2026-08-07T06:00:51.841420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.841420Z digest=sha256:932ecf8429166c50044b43123720889a1b1f58062cff9df036bd00d3a5705f08

Observation 632d4784-38a3-402a-a7e8-1e30c44690f5 · outbound

This paper cites Graph spectra and the detectability of community structure in networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Graph spectra and the detectability of community structure in networks

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.708868Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.845913Z digest=sha256:810fa57a9b98663982b63b83ce68ca3ef73e94d830187a750b1a0f6f1d38ee73

Observation 708f47bc-fe77-41ab-aa44-50896327ba50 · outbound

This paper cites Nonlinear random matrix theory for deep learning.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Nonlinear random matrix theory for deep learning

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.696398Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.849549Z digest=sha256:4ddc2677d5acaf2a79adc43eb6a9882797fad865d0cb5453370f34b62f942003

Observation 744d6b0b-ed8c-4240-ab1c-b2f5e709fced · outbound

This paper cites AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.852982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.852982Z digest=sha256:fc52da3757ac1e6cf0b7ba054c4629b8099b81efe0fee59c21020e1ff47c22f5

Observation eb84b51b-2809-43a3-8810-e1849c8281b3 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Winogrande: An adversarial winograd schema challenge at scale

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.856552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.856552Z digest=sha256:43c81c8fce77a7e37bd359d9519f935f7fc67a7747b13c7324dbec44c7783418

Observation 154b86d8-c19b-42e9-a583-5864e3a61a2a · outbound

This paper cites Stable Rank Normalization for Improved Generalization in Neural Networks and GANs.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.860107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.860107Z digest=sha256:82c5dd8e95d1282604251bac7fe57f349fee6362174fa170c14c91831b697bb6

Observation bf5b3c2e-1c94-40b0-ab48-ae90404a3087 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 47

Resolution
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no resolver link, observed 2026-08-07T06:00:51.863964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.863964Z digest=sha256:f9f7e07c1e6f0df9bd8eb54b48c453b8e335e012fdfb88de0503ed32c84fbcec

Observation b53b2516-bd26-4c69-9e5a-016d0dc37f82 · outbound

This paper cites A tail-index analysis of stochastic gradient noise in deep neural networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias A tail-index analysis of stochastic gradient noise in deep neural networks

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.867767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.867767Z digest=sha256:b214c0d808697f82004f973a1611a2a563b2fe4ed9f33cbe62265ffdd9dbd16d

Observation ea37a1a5-30e8-4607-aa0b-33fb9db059e5 · outbound

This paper cites Hausdorff dimension, heavy tails, and generalization in neural networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Hausdorff dimension, heavy tails, and generalization in neural networks

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.667546Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.871173Z digest=sha256:7dfb78483250ec98a57f2bd04ebbecd5415f80e760fcb49b0f980b584535b239

Observation 762e8aa0-feef-47a5-9815-7a3360b93b34 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias A Simple and Effective Pruning Approach for Large Language Models

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.874740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.874740Z digest=sha256:f1cfb4a63eecf53edd5eecd29572d7aa4694b50e40d32d23c51dd7c2b45b4357

Observation 1d1f3918-3412-4211-b523-693144cf8601 · outbound

This paper cites Pdebench: An extensive benchmark for scientific machine learning.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Pdebench: An extensive benchmark for scientific machine learning

Reference 51

Resolution
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no resolver link, observed 2026-08-07T06:00:51.878664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.878664Z digest=sha256:2b4d550ed9dce891e3403e7f31dd44f39d6ba27a081e8f8fa09e7f445a025b32

Observation d9d77f4e-3809-46ce-a750-5c70a0c0ced9 · outbound

This paper cites Topics in random matrix theory, volume 132.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Topics in random matrix theory, volume 132

Reference 52

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no resolver link, observed 2026-08-07T06:00:51.882215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.882215Z digest=sha256:b26270fbcd104fc9434b59ce030a39f93802c7e1e8d75ba362e02c1fcc922619

Observation c96c9f74-da76-4aa9-bb53-120b22903b5d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias LLaMA: Open and Efficient Foundation Language Models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.885978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.885978Z digest=sha256:a54d030ea01743b3522f6af8e652e4d247dd0ecc8e86ce65ba33f38f4f22df3e

Observation a26140bb-49b8-448c-929f-9b4f6fbf748d · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.890034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.890034Z digest=sha256:6764e3432f456fea542cbb26da0e6136d09632ba035355ecb6e9eaa664a65f2f

Observation b0cb3bab-38ca-4253-9122-3a4677239a24 · outbound

This paper cites Tulino and Sergio Verdú.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Tulino and Sergio Verdú

Reference 55

Resolution
verified exact
doi, observed 2026-08-07T06:00:52.011695Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.893958Z digest=sha256:e5cd8e131eadb6f3e3a6041f8fd5ecb77d3d83a34b8404e6d9f3c012ada64235

Observation d3e1297c-6380-4b2c-bd80-8e5e7b7858c5 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.897937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.897937Z digest=sha256:a67b882a8ed37a307c9351df74525266860e99308f5f48c00c1ed26b4d8ab959

Observation f74a8f92-4d78-4889-8e64-667299ae6b75 · outbound

This paper cites Spectral Evolution and Invariance in Linear-width Neural Networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Spectral Evolution and Invariance in Linear-width Neural Networks

Reference 57

Resolution
verified exact
local_arxiv, observed 2026-08-07T06:00:52.295210Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.901852Z digest=sha256:2c9d6bb1ad9e80e25779cfb7da48258f897d840314390c79b59d25b377f7ed3e

Observation 872597a2-1652-4265-933d-32899998335d · outbound

This paper cites Safe Multi-agent Reinforcement Learning with Natural Language Constraints.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Safe Multi-agent Reinforcement Learning with Natural Language Constraints

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-07T06:00:52.276686Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.906214Z digest=sha256:11737019eafe41aa533343f0f9b28566593e5265c66bb1b25f2805184f3804bd

Observation 091d8f56-5113-4809-a69e-27e4f77700fc · outbound

This paper cites M3hf: Multi-agent reinforcement learning from multi-phase human feedback of mixed quality.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias M3hf: Multi-agent reinforcement learning from multi-phase human feedback of mixed quality

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.638534Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.910500Z digest=sha256:f0c4ddb3e648d6e07f03254a75bf3c8d9ff164783431672f445e5222890a2e79

Observation 080b48e7-3b2d-4bfb-88e9-31aae79215b6 · outbound

This paper cites Tensor programs iv: Feature learning in infinite-width neural networks.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Tensor programs iv: Feature learning in infinite-width neural networks

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.914356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.914356Z digest=sha256:185f5fce1e7bcc026f1df1fad946d3b604d14dd36cbf6d514fd0f40cba769755

Observation 697b68ac-94da-4602-88c9-c39d7c8cf124 · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.918076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.918076Z digest=sha256:005635c588b09198b61af1e9fd47588ed0af6db9a697f0c3df4bda174189cf1b

Observation f151b568-8217-4b12-8117-9873201dc6ca · outbound

This paper cites Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.922284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.922284Z digest=sha256:ed68a4bf705ca7aa6269645a0e1b1b52f32e9243ba34e322b0b6d5a7981bafef

Observation 4db911bd-4f09-498a-92de-43940081b30c · outbound

This paper cites Multimodal commonsense knowledge distillation for visual question answering (student abstract).

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Multimodal commonsense knowledge distillation for visual question answering (student abstract)

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.617248Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.926420Z digest=sha256:64c44a5d766a2495e2fb374bfd91a670c5f25a08ce1ec648f53180b3bea93983

Observation 14ceb5be-eff4-4fdb-a232-4850c8e0dbd9 · outbound

This paper cites MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering

Reference 64

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unresolved
no resolver link, observed 2026-08-07T06:00:51.930988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.930988Z digest=sha256:8969547e8cefa1dd96b36f05c9f4afb4907f2ce8ba722c22560cb9619354af2c

Observation 6c92a131-5f55-4467-8f83-2e87176298cb · outbound

This paper cites Gonzalez, Kannan Ramchandran, Charles H.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Gonzalez, Kannan Ramchandran, Charles H

Reference 65

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unresolved
no resolver link, observed 2026-08-07T06:00:51.935251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.935251Z digest=sha256:f035cdc9ac70a00263472a34c80600669efac76884080c00eb6513f7aabd92c1

Observation 52659e9c-dcc0-4cf0-ae10-318c363af065 · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.939296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.939296Z digest=sha256:f444de92ea7310d6f93f8e4d5146e90a2e24e200276bb7cec32ecb1cff29aba2

Observation 9fb287c6-7cc8-48ce-ab15-3f949fa6e9a2 · outbound

This paper cites Spectral Norm Regularization for Improving the Generalizability of Deep Learning.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Spectral Norm Regularization for Improving the Generalizability of Deep Learning

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.943686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.943686Z digest=sha256:4556202c1ef23ca57460838580fe0971b8a56acce3b8861853d265c6e55eeeb6

Observation 3ec66ec6-3ff3-4cd8-8906-5e6c9977da53 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.947758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.947758Z digest=sha256:439447ec465e031e12a185000ac0621de2430ac545aa94d0207e4a321e142202

Observation 10a65845-56f2-4216-946d-cdc327725998 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias OPT: Open Pre-trained Transformer Language Models

Reference 69

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unresolved
no resolver link, observed 2026-08-07T06:00:51.952130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.952130Z digest=sha256:e92d6da0b1bd6d39ebb3f5299cbe1b33e2494111c724c63e19fc266e92858014

Observation 1d3e3b2d-0071-41d3-b559-5f9c4f5870b9 · outbound

This paper cites Temperature balancing, layer-wise weight analysis, and neural network training.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Temperature balancing, layer-wise weight analysis, and neural network training

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.604603Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.956252Z digest=sha256:9d83835871e22cb80b569646ecb1f86e2bc5e2db331e078f98c4792fc52a353e

Observation dbf735e7-d669-4466-9c83-cbdbe3856d02 · outbound

This paper cites Remedy: Recipe merging dynamics in large vision-language models.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Remedy: Recipe merging dynamics in large vision-language models

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.591848Z

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.

source=arxiv_source observed=2026-08-07T06:00:51.960314Z digest=sha256:b8fa4506e933fe92110b0e03b8705c5e8f505b0e69873813b1a7c06bb0cc1eff

Observation a06a3613-6153-410f-9c7b-4f180a284af3 · outbound

This paper cites @esa (Ref.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias @esa (Ref

Reference 72

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unresolved
no resolver link, observed 2026-08-07T06:00:51.964187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.964187Z digest=sha256:6a3b1c94aeb2b8f1c7e1a2c129424389d4dd2a0f03276c41a50f1ca8d92995e4

Observation ff7bb1ec-01dd-46c3-991f-c446b611261f · outbound

This paper cites an unresolved cited work.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Unresolved cited work

Reference 73

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unresolved
no resolver link, observed 2026-08-07T06:00:51.968644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.968644Z digest=sha256:e2dcd484e4d8538e935e4c3d2d5256d87a216dd9008b051fb195a9f3e67d3d29

Observation 45b6c17d-ec5a-4d0a-9971-cfd869263ad5 · outbound

This paper cites training quality.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias training quality

Reference 74

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unresolved
no resolver link, observed 2026-08-07T06:00:51.972799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.972799Z digest=sha256:ad21c50891d514c2545beb675bba3e3a0558b25aaff640fb61a5261a6cc0da48

Pith citing papers

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S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner cites this paper.

S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias

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local_arxiv, observed 2026-08-05T18:12:37.398888Z

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source=arxiv_source observed=2026-08-05T18:12:34.296158Z digest=sha256:62cfc70f72006dd1db7b0f16833e416fd90a95ee93c19257989c280f520c96f4