Paper Citation Record · LEDGER

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models

As of 6 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2605.26895.

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

pith.paper-citation-record.v1
2605.26895 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:56:54.047126Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T10:14:12.034776Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T20:30:07.719081Z

Reference resolution

55 of 55 outbound references displayed

verified exact19
verified fuzzy0
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f6d6abf6-c617-43bd-99f5-e0a0db965c59 · outbound

This paper cites On the optimization of deep networks: Implicit acceleration by overparameterization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models On the optimization of deep networks: Implicit acceleration by overparameterization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be1ed8c9-061f-47e1-8c2b-2fa5736ea1c1 · outbound

This paper cites Layer Normalization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Layer Normalization

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.377843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7740af1e-5abc-4fa9-ab43-027344c3c110 · outbound

This paper cites Optimization methods for large-scale machine learning.SIAM review, 60(2):223–311, 2018.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Optimization methods for large-scale machine learning.SIAM review, 60(2):223–311, 2018

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73c0ad59-05ac-41fc-aef6-a8cf0d736575 · outbound

This paper cites Seednorm: Self-rescaled dynamic normalization.arXiv preprint arXiv:2510.22777, 2025.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Seednorm: Self-rescaled dynamic normalization.arXiv preprint arXiv:2510.22777, 2025

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.392947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c24cae3c-4c71-49f5-bb39-cd4dbb4baa3b · outbound

This paper cites Post-layernorm is back: Stable, expressive, and deep.arXiv preprint arXiv:2601.19895, 2026.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Post-layernorm is back: Stable, expressive, and deep.arXiv preprint arXiv:2601.19895, 2026

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.354792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a0381982-7521-4446-a942-06f7f078b0d3 · outbound

This paper cites Label noise SGD provably prefers flat global minimizers.Advances in Neural Information Processing Systems, 34:27449–27461, 2021.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Label noise SGD provably prefers flat global minimizers.Advances in Neural Information Processing Systems, 34:27449–27461, 2021

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 563167d2-5db2-4da5-97c5-274a4b6bcf0c · outbound

This paper cites Scaling vision transformers to 22 billion parameters.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Scaling vision transformers to 22 billion parameters

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aad33d69-f219-4309-9bc4-20206e40352d · outbound

This paper cites The inverse variance–flatness relation in stochastic gradient descent is critical for finding flat minima.Proceedings of the National Academy of Sciences, 118(9):e2015617118, 2021.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models The inverse variance–flatness relation in stochastic gradient descent is critical for finding flat minima.Proceedings of the National Academy of Sciences, 118(9):e2015617118, 2021

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa54f2e4-338c-4884-a997-1a193ffbe27a · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Shampoo: Preconditioned stochastic tensor optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe55fa24-f041-4dd1-9ed0-1e0cf2177635 · outbound

This paper cites Shape matters: Understanding the implicit bias of the noise covariance.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Shape matters: Understanding the implicit bias of the noise covariance

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be4249ca-42b4-40b0-bd1c-8f5e91ffe824 · outbound

This paper cites Introduction to online convex optimization.Foundationsand Trends®in Optimization, 2(3-4): 157–325, 2016.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Introduction to online convex optimization.Foundationsand Trends®in Optimization, 2(3-4): 157–325, 2016

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c3e6461-6baa-48d5-bbc6-9586e4540a15 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Training Compute-Optimal Large Language Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.389862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8086825e-b5f4-4692-b21d-3ce67c91b14d · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.367612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4f71305d-73b7-4d70-882e-fca01f58e58b · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.360360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e5b62692-d2c3-4f98-9aad-1a19e2bd022a · outbound

This paper cites an unresolved cited work.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fa44733-cacf-498b-899e-a32e52f58720 · outbound

This paper cites Muon optimizer.URL https://github.com/KellerJordan/Muon?tab=readme-ov-file, 2024.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Muon optimizer.URL https://github.com/KellerJordan/Muon?tab=readme-ov-file, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0795519-9065-46c4-9819-74de45bd95e2 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Adam: A Method for Stochastic Optimization

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.387598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e762af73-3c2d-4a56-aa4e-20f81ae699a5 · outbound

This paper cites Stochastic modified equations and adaptive stochastic gradient algorithms.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Stochastic modified equations and adaptive stochastic gradient algorithms

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3802c18e-7dd9-4db0-a8fd-24a9e2427693 · outbound

This paper cites What happens after SGD reaches zero loss?–a mathematical framework.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models What happens after SGD reaches zero loss?–a mathematical framework

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d697157-3b3d-4a0d-84ea-dae74da3b441 · outbound

This paper cites Muon is Scalable for LLM Training.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Muon is Scalable for LLM Training

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.382885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ccd897ff-7d7d-4b49-916b-c6a55070a280 · outbound

This paper cites Noise and fluctuation of finite learning rate stochastic gradient descent.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Noise and fluctuation of finite learning rate stochastic gradient descent

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eee105d-aa22-4d74-a3f1-9c07e298930e · outbound

This paper cites Decoupled Weight Decay Regularization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Decoupled Weight Decay Regularization

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.362868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8bdfbc38-ee2b-4c5d-a588-e30c4a66f3f1 · outbound

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

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Optimizing neural networks with kronecker-factored approximate curvature

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 229fa5f6-b91b-4260-b47b-b7cfe68eb540 · outbound

This paper cites Power-law escape rate of SGD.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Power-law escape rate of SGD

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.372698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 43dc79a1-f30a-4837-8e97-59f363d273e7 · outbound

This paper cites Power-law escape rate of sgd.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Power-law escape rate of sgd

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a20dea62-5991-4a16-9724-79f52a353b8b · outbound

This paper cites Transformers without tears: Improving the normalization of self-attention.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Transformers without tears: Improving the normalization of self-attention

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86de0c30-2a11-40e6-8cd7-179624a610be · outbound

This paper cites 2 OLMo 2 Furious.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models 2 OLMo 2 Furious

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.370226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d02f29f-e9e8-4d49-926c-a25aca2057fd · outbound

This paper cites A unified view of attention and residual sinks: Outlier-driven rescaling is essential for transformer training.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models A unified view of attention and residual sinks: Outlier-driven rescaling is essential for transformer training

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.348937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bc063411-3758-4471-bda5-d7e09e89a366 · outbound

This paper cites Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 781a9a10-2291-4cab-94c9-b55e86dea0ba · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.International Conference on Learning Representations, 2014.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.International Conference on Learning Representations, 2014

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa631fa3-04d9-462f-8e79-a12d46d152ca · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8152584-9a2d-4647-9b51-e1493635dfdb · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.357522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ac12bede-7af8-429f-a75e-bdeab9238f83 · outbound

This paper cites Gemma 3 technical report, 2025.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Gemma 3 technical report, 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9400ecb-df4b-4eb8-9543-5a1a02d9ed55 · outbound

This paper cites Tieleman and G.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Tieleman and G

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ef7b968-45f8-458c-b16f-d722427c8cc0 · outbound

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

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.365142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9f20bf04-4300-44b0-8ac3-b13f92c56f58 · outbound

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

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Attention is all you need.Advancesin neural information processing systems, 30, 2017

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5524de1-3089-4f4f-89b7-d32b035335e4 · outbound

This paper cites Soap: Improving and stabilizing shampoo using adam for language modeling.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Soap: Improving and stabilizing shampoo using adam for language modeling

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f927861a-c287-4f22-8414-a935da506baf · outbound

This paper cites Deepnet: Scaling transformers to 1,000 layers.IEEE Transactionson Pattern Analysis and Machine Intelligence, 46(10):6761–6774, 2024.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Deepnet: Scaling transformers to 1,000 layers.IEEE Transactionson Pattern Analysis and Machine Intelligence, 46(10):6761–6774, 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6ce5507-9cde-4779-b69e-f99be7ff6771 · outbound

This paper cites The sharpness disparity principle in transformers for accelerating language model pre-training.International Conference on Machine Learning, pages 64859–64879, 2025.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models The sharpness disparity principle in transformers for accelerating language model pre-training.International Conference on Machine Learning, pages 64859–64879, 2025

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6704bdd-adb1-4559-a6db-bb215eefb6bf · outbound

This paper cites Gradpower: Powering gradients for faster language model pre-training.International Conference on Machine Learning, 2026.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Gradpower: Powering gradients for faster language model pre-training.International Conference on Machine Learning, 2026

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21d359c4-b7af-48de-9e93-613595c7e38c · outbound

This paper cites A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T20:03:56.375366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 492f1ed6-efa2-4daf-823c-e2410eff2bad · outbound

This paper cites Improving generalization and convergence by enhancing implicit regularization.Advances in Neural Information Processing Systems, 2024.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Improving generalization and convergence by enhancing implicit regularization.Advances in Neural Information Processing Systems, 2024

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf9f3de2-31a2-420c-bd45-26326b1cff4a · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Bayesian learning via stochastic gradient langevin dynamics

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 832087ea-8f8e-47c5-bc71-c2ec5f8c04b1 · outbound

This paper cites Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.380331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a5ca6b9c-f9f9-4c65-b800-16c329c1264c · outbound

This paper cites The alignment property of sgd noise and how it helps select flat minima: A stability analysis.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models The alignment property of sgd noise and how it helps select flat minima: A stability analysis

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0d7d435-572a-49e8-9dec-ddf14da7dabd · outbound

This paper cites On layer normalization in the transformer architecture.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models On layer normalization in the transformer architecture

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a64532f0-4ff0-4ae7-b228-00a36c377464 · outbound

This paper cites Qwen2 Technical Report.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Qwen2 Technical Report

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.385040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2224e8a4-c0cb-4f8f-b368-d9ab2f785c05 · outbound

This paper cites Qwen3 Technical Report.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Qwen3 Technical Report

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-06-29T20:03:56.351806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6445e9cd-4947-42aa-ad06-3609600da68a · outbound

This paper cites Scaling vision transformers.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Scaling vision transformers

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea413e91-d95c-498c-95b7-d2863b9f0c20 · outbound

This paper cites Root mean square layer normalization.Advancesin Neural Information Processing Systems, 32, 2019.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Root mean square layer normalization.Advancesin Neural Information Processing Systems, 32, 2019

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b3fa41-7013-45d3-b3c8-b746c5dfed62 · outbound

This paper cites Transformers without normalization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Transformers without normalization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8333a4a-3b2d-479b-9b98-5206ef6a6822 · outbound

This paper cites arXiv preprint arXiv:2602.22681 , year=.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models arXiv preprint arXiv:2602.22681 , year=

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.343296Z

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Observation b67ff9bd-9274-440b-812b-594310558c26 · outbound

This paper cites HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:03:56.345993Z

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Observation 3a4233b1-3f6a-4b7e-8454-87f75ec667cc · outbound

This paper cites Parameter symmetry and noise equilibrium of stochastic gradient descent.Advancesin Neural Information Processing Systems, 37:93874–93906, 2024.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Parameter symmetry and noise equilibrium of stochastic gradient descent.Advancesin Neural Information Processing Systems, 37:93874–93906, 2024

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

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Observation 92c2c495-d03c-4e03-b568-cdf8b149eb2e · outbound

This paper cites Sincew=0π-a.s., we also have a=γ⊙w=0π-a.s.

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models Sincew=0π-a.s., we also have a=γ⊙w=0π-a.s

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-29T19:56:54.047126Z

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Pith citing papers

Observation 5ccad840-ac3a-4815-864f-33ebe42ec849 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models

Reference 112

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T20:30:07.720403Z

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Observation 85f586ff-f2a5-42b6-89f4-830783f91b0d · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-02T10:14:12.034776Z

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