Pith. sign in

Paper Citation Record · LEDGER

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias

As of 10 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 2026-08-09T06:31:02.800959+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
  • malformed identifier0
  • metadata mismatch0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:53.022161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.697796Z digest=sha256:f5b86f700e731bc32d7c29319a96c4e7bc9005f28171c2efd911ff9fdcadea25

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:53.008834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.702376Z digest=sha256:11edb7d2f323f62252f4b41f9a08fdde8ff8484c66d2021d2ae5dbd78b70c6fd

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.995793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.706087Z digest=sha256:64cd553b23c429fcdc96478943ee65ffd7e87ca01c012fc242d3d5bb9cdce812

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.983220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.709820Z digest=sha256:f22685ad83ac819f2d2eaf969d98b1b502895951399edd7acbe9cf5d5515982e

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.713432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.713432Z digest=sha256:28a511bb517be726a3e1d07f19aef41e06676decfcaa2c653f61ad1659878530

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.970709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.717469Z digest=sha256:8af1c35e992e74b6196d4f62d2a28416ce0ad3ef10f60e4daf298be770d04ca5

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.721613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.721613Z digest=sha256:bc1891006ccf96f64f847749151d013293ed7af36fceb33a94e68ff705f59d15

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.725336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.725336Z digest=sha256:77a66c9352c9f81171fd33aff2ae7309e7bad1ed707b7556f9c1d5dc10f306f2

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.729073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.729073Z digest=sha256:d91c4b3bed481d0f7ab34e68c5539cb9c3057d995a62cb740e5c011a1608de5f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.949433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.732669Z digest=sha256:3ff8e65e316d4712ef9dad978580ffdd83789a198434bc9d016a47d7927687a3

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.936927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.736091Z digest=sha256:b6757743923ccf88f74a07578a2e3aed71ea1333aa18b59f5a6c0c791c4eef2f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.923709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.739518Z digest=sha256:0b3960663eb14ee1083df6675aa29127a377105b1665061414b4a888a98b2839

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.742816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.742816Z digest=sha256:7fdd927fb1be16886ab60da81909e4ff23a10a2caca41b9440d238c896dcfbe1

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.911350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.746431Z digest=sha256:7f8ac8ee5966012d4063d68d801b26d9815c3096c951d530baac10ea3c8e1d6c

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.749774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.749774Z digest=sha256:9fd6914210e9ba32c42bf4769da4c7ac37a5a78c70f27754e5c130a1de31c588

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.753149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.753149Z digest=sha256:ecefe3bb5f333f59d1b91e54166e0df6d89cada56452ff37fe5387a07cdc1a3e

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

This paper cites Weidenmüller.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Weidenmüller

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.756863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.756863Z digest=sha256:ad2358434aa8f87276b221433cf89893127714a2ee0e8dc538fe17f6bf5b59ec

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.760473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.760473Z digest=sha256:60fb652fe88b2bc31c4596023c2993d8a2e4b0b22ac7674ebf1251d2b051e70c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.882249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.763798Z digest=sha256:377fdf553818b5b86b9144484bb78ed38ce817a69433ce280bb145188ce995af

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.767501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.767501Z digest=sha256:e6d881a60c81cd21a2eafbdf9eb66a4855c035c2b546e87b0f5cba740cd1e251

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.868940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.771183Z digest=sha256:3521121e478c70c28cebf7b15009758f8c6263238a40a868858f106bc052eb66

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.855730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.774488Z digest=sha256:3c1eb9e4c39f599731a81c44a14efeb30881f6e43084fd85a275530d1de42e9b

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.778001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.778001Z digest=sha256:878f8651be02703d9f1bf0f422c91d0ddb3450f93983b9164765d48393f98174

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.834148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.781338Z digest=sha256:0d0ea600bbeb372db47a293ecaf235d65d0393d1ea0ce916ed5725b69b437699

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.821406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.784739Z digest=sha256:87da1e34460541a2ceae12c2a33a5ab4c5c319445b9ed5283848d09ba0990ef4

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.808590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.788186Z digest=sha256:9269e9a58f3309ab4d0b27dd37c535cf48c4a2c8f32dbd39cd060891bda210d3

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

This paper cites Universality laws for high-dimensional learning with random features.

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

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.795909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.791721Z digest=sha256:4067cf6ff6d8e0ff33d49ff7387c731a0e7661294e08ef74329aacfaccedaa4b

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.795047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.795047Z digest=sha256:a8dd833ed1e990377fe60ede8cc1d7a49c48edf2e080993f4fb8ca4da3ce922e

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.798485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.798485Z digest=sha256:2451904216514b638a3a71cd31a05f751e97a595a67913d0e7f08d3fd8f0325c

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

This paper cites BaWA : Automatic optimizing pruning metric for large language models with balanced weight and activation.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.775090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.801779Z digest=sha256:d31898c7f45482ffd1914b8dafdf88d0fa2102c791b59f0dbc47535c9b4912f8

Observation 2aa71a65-f902-44b3-8afb-213cf0756125 · outbound

This paper cites Model Balancing Helps Low-data Training and Fine-tuning.

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

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.805197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.805197Z digest=sha256:9a1622be59cf5065f68d39d57a916bd4df4fb56ee23abcd11f4d814811c7a3dd

Observation d80cfb40-4173-4dc2-894f-dea92483912a · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.761941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.808746Z digest=sha256:12c9296589f9427052ff48794ef762b68fdb6ec023ea488b0f77e88c69a3f6a2

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.812211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.812211Z digest=sha256:3139aabc8125171adf1a5696501533b09d5afd0fe8b342c4ad23ea4de3565a53

Observation 62bfd72d-bddc-4a8c-8c7c-12af1970c10c · 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 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:00:52.747702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.816243Z digest=sha256:53418cff4919943f822cc4bc9656d11ffeb8c05ebc90c6527ba433a87058b81c

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.819696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.819696Z digest=sha256:e1e4e8dfb89b44669c58030c59fd8490c03b73deb3ec3029334e38842bab0a9e

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

Resolution
verified fuzzy
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.823349Z digest=sha256:95c64be372300012a396b90e585b2fc84aed8e8038e9679bbd2b6869e53995a7

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

Resolution
unresolved
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=sha256:08e86198401a9a3ce2693adf8a0184bf30740db413c21a3dd5299df58e61c4b5

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.830605Z digest=sha256:81f3829d48ea6123958d07480900d856b0727ef7f85955b7e349d90d2c2564ff

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=sha256:66e068196e324c69a9837ac4006f880448acdec446b990316c57007b7b4132b1

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

Resolution
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=sha256:25d45433be2f5164738ac5b5965e5accb00a1f8c149946728d68514450289494

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
unresolved
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:e04e44684c07bf937a0b2a390107ca9278fb03790de7206c196ad995da1b344e

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.845913Z digest=sha256:0dd90fcde92e6fd99b602ce7804cfd9c86b6f2ccb2fed4220e0b84ab7604e2bd

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.849549Z digest=sha256:f98b4adbc37ffc91748d54962a3623be991d49492ab26cc95d605ad91a3fbc1c

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:fabad39dde0b29a40b1780ee8ecbb01fa76d281b5c220ad6dfa997769599d621

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:c170c6ac0b08517f3ccd846d86f672854a7a2fec4fbc521cdfb49f33c929f60f

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=sha256:874d42d99600375f689b0694987bcfd2cc55c26b9e2dbb6ceeedf70e305e805d

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
unresolved
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=sha256:7e8f3e8d6429944ad884311b478d1fa68023eee5eab7fe29ebb9644283a8aa02

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=sha256:8d42d9beaa07ecf6bdd638b636331de074b55c849d4b3d07ba05e7116181a74e

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.871173Z digest=sha256:1b0f4a417fd0aa71a11178629e13f6198055ccc72e5b84ce0dc58e7d606141d1

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:e0b05d015dc674393bac9f355cd340cd8f23952254a5726ac3d4bf97fd9fc7c3

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
unresolved
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=sha256:86d8db8fd6837598873970ccef349b52fe676d20879f197a8cf9f4c905ee5223

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

Resolution
unresolved
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=sha256:525a5f2da67b4ccf138d89c66cb335388041af82c68e68a44b965ed9c1a7c41e

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=sha256:9c365fa1f0028960effe93a81945fe78eefc785be0d2bfa882aafa6858470af2

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=sha256:24e75355a9f7116bae05f5320bf0f0fbb3048ff36e74ef159ddc0fb76b63e676

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.893958Z digest=sha256:8d1bdb96760fe1b0d867507244b5c1e8d2217cd100a40528e0d291db15fde7bf

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=sha256:9283d21f8e3d7781ecf744982634b3ff967b7bc751b7df5f83d2ef18cbc35e0f

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.901852Z digest=sha256:29a996b85df0898512c9bfae573dfafc151566b00168a9cacd43b1380dabf427

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.906214Z digest=sha256:606be195da0581d21da984ecd7d8d6157d1ae41b4d9ddf4be12a9a32bb17be9e

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.910500Z digest=sha256:8842d764da92ed678bd1db78eec8e2aa42667d9d318ebb87b2e2118953f99256

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:ddec864ca110de7230a2181084f6940b5456b60b1802c8dc9c4a43ef161c3917

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=sha256:855e7c05d0674b286eeaef6e5d3c40486c28775ecf30e4af4cae01e312dcb524

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=sha256:21b0a33ad997c868bec33d0f1c066b190f4b0a7d6fd6bab3ea24acbf9d2646a3

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.926420Z digest=sha256:53d9ca0a69abbd6baea374cff90b76cd49da224f1e7ab2376adbb384890c5b82

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

Resolution
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:f55005fa7141825a88607696c816ef1714ffabf525a89b1b0f2e80180f969892

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

Resolution
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:c2d9b8abb75a7f83d8b5b19ceb0acdfd9424bd767e06a8567bf289660092f1d3

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=sha256:28ba9eb4be1f8e6fa33e38fbbd772a52febe040d0e074620379414396ad5ae19

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:e92d7529feeb269875bf66a800dcb3f246ac7f49d6b57b85b1fcaa830a1bed90

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:da932e633f073e3642683bd0521199f6171f0cf776b4ebccad793a8a421095ad

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

Resolution
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=sha256:078f6e2020f986dcf982cbb1d7ee4b6cb4cacdc57c1efeae94099e60872d2978

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.956252Z digest=sha256:28220ba620e22bb04efde2c1a57b33c8034a1f4faf6f06faee4add92f777aa70

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T06:00:51.960314Z digest=sha256:4ca8a65174e149511fee6a068b261d383de0cef5a21d35544c695f38363f8a24

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

Resolution
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=sha256:402a8bd2299f6677363df1e86cfe950d2fd1f32c5a4cb63210fa91f1fa6b53c8

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

Resolution
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=sha256:8c171fc30307817fb7f266b0468c4faac2810cb14f148405bd0a855010ac6953

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

Resolution
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=sha256:476e5453b053b3765c932bf23bcb59caa6776b419f8d80552c09c3634dbc4ce8

Pith citing papers

Observation 4f7a8ce7-cf02-445f-9d95-afe313e114ac · inbound

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

Reference 14

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T18:12:34.296158Z digest=sha256:9d9e20656bc9a77b6c35038e4a89d97024620ccbffb992cd750ab8d1812b88fc