Renormalization Group

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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The Row Normalization Puzzle in Muon
Sep 30, 2026Jiayu Zhang, Tianyi LinMuon Normalization
Department of Industrial Engineering and Operations Research Columbia University
Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction
Sep 28, 2026Burc GokdenInference Dynamics Renormalization Group
Fromthesky Research Labs LLC, Oregon, USA
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Jul 30, 2026Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2Graph Neural Networks Renormalization Group
TU Delft · University of Seville · University of Barcelona +2
Effective Parameters, Real Behavior: Renormalization for Robotics -- From Infinite Electron Mass to Sim-to-Real Gap
Jul 27, 2026Youran Sun, Jiaxuan Guo, Xingyu Ren +2Sim-To-Real Gap Renormalization Group
University of Maryland, College Park · Stanford University · The Chinese University of Hong Kong
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
Jul 1, 2026Shaoheng Zhang, Zhichen Li, Jie MeiVision-Language Navigation Frozen Vision-Language Models
School of Intelligence Science and Engineering Harbin Institute of Technology, Shenzhen Shenzhen, China · School of Computer Science and Technology Harbin Institute of Technology, Shenzhen Shenzhen, China
Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua
Jun 29, 2026Raul Jimenez, David Mateos, Pavlos Protopapas +3Effective Field Theory Renormalization Group
Institut de Ciències del Cosmos (ICC), Universitat de Barcelona, Martí i Franquès 1, ES-08028, Barcelona, Spain. · Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, ES-08010, Barcelona, Spain. · Institute for Applied Computational Science, Harvard University, Cambridge, MA.
Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance
Jun 16, 2026Evgeny Nikulchev, Dmitry IlinFault Diagnosis Renormalization Group
MIREA – Russian Technological University, Moscow, Russia
Rank Collapse, Fixed Points, and the Renormalization Group Structure of MLP Residual Networks
Jun 9, 2026Parviz Haggi-Mani, Irina RishResidual Networks Renormalization Group
Universit´e de Montr´eal · Mila - Quebec AI Institute
Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
May 29, 2026Arpan Biswas, Sutirtha Paul, Joseph Agada +2Many-Body Physics Gaussian Process
University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Institute for Advanced Materials and Manufacturing, University of Tennessee, Knoxville, Tennessee 37996, USA · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA. +2
Interpreting FCDNNs via RG on Exponential Family
May 29, 2026Fuzhou Gong, Zigeng XiaRenormalization Group Exponential Family
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. · School of Mathematics, Northwest University, Xi’an 710127, China.
Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime
May 28, 2026Paolo Baglioni, Christian Keup, Vincenzo Zimbardo +4Bayesian Neural Networks Renormalization Group
1INFN, Sezione di Milano Bicocca, Piazza della Scienza 3, 20126, Milano, Italy · 2INFN, Gruppo Collegato di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy · 3Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Universit`a degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy +2
A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training
May 14, 2026Andrea Angino, Bindi Çapriqi, Shega Likaj +2Spectral Preconditioning Trust Region
UniDistance Suisse · King Abdullah University of Science and Technology · Universit`a della Svizzera italiana
The Silent Vote: Improving Zero-Shot LLM Reliability by Aggregating Semantic Neighborhoods
May 10, 2026Sanket Badhe, Priyanka Tiwari, Deep ShahGumbel-Softmax Relaxation Overconfidence
Google
Controlling Transient Amplification Improves Long-horizon Rollouts
May 9, 2026Adeel Pervez, Francesco LocatelloAutoregressive Rollout Long-Horizon Forecasting
Institute of Science and Technology Austria Klosterneuburg, Austria
Lecture Notes on Statistical Physics and Neural Networks
May 7, 2026Olaf HohmStatistical Physics Spatial Photonic Ising Machines
Institute for Physics, Humboldt University Berlin, Zum Großen Windkanal 2, D-12489 Berlin, Germany
A renormalization-group inspired lattice-based framework for piecewise generalized linear models
May 6, 2026Joshua C. ChangGeneralized Linear Model Renormalization Group
NIH Clinical Center, Rehabilitation Medicine · Mederrata Research Inc · Sound Prediction Inc
Kohn-Sham Hamiltonian from Effective Field Theory: Quasiparticle Band Narrowing from Frozen Core Dynamics
Apr 28, 2026Xiansheng Cai, Han Wang, Kun ChenDensity Functional Theory Effective Field Theory
Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
Data-Driven Variational Basis Learning Beyond Neural Networks: A Non-Neural Framework for Adaptive Basis Discovery
Apr 17, 2026Andrew KirulutaBasis Functions Unsupervised Dictionary Learning
UC Berkeley, CA
Collective Kernel EFT for Pre-activation ResNets
Apr 17, 2026Hidetoshi Kawase, Toshihiro OtaResidual Networks Renormalization Group
CyberAgent, Inc., Shibuya, Tokyo 150–6121 Japan
WavePhaseNet: A DFT-Based Method for Constructing Semantic Conceptual Hierarchy Structures (SCHS)
Feb 16, 2026Kiyotaka Kasubuchi, Kazuo FukiyaSemantic Representations Hierarchical
[affiliation]
Stabilizing Native Low-Rank LLM Pretraining
Feb 12, 2026Paul Janson, Edouard Oyallon, Eugene BelilovskyLow-Rank Structure Pretraining
Concordia University, Montreal, Canada · Mila Quebec AI Institute, Montreal, Canada · Sorbonne University, CNRS, Paris, France

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The Row Normalization Puzzle in Muon

Jiayu Zhang, Tianyi Lin

Department of Industrial Engineering and Operations Research Columbia University

Muon Normalization Renormalization Group Worst-Case Momentum Exact+1

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This paper examines how row-wise renormalization affects Muon, focusing on the gap between NorMuon's worst-case guarantees and its practical performance (Li et al.). Despite its growing adoption and promising performance in large language model (LLM) pretraining, NorMuon's worst-case guarantees remain poorly understood. One fundamental question is: Does row normalization yield provable convergence gains, potentially through its interaction with approximate polar computation and exponential moving-average momentum? Our results show that row normalization introduces a dimension-dependent factor in the worst-case iteration complexity under the operator-norm geometry, which persists even with exact polar computation and any fixed momentum parameters. Indeed, we establish an algorithm-dependent lower bound and a matching upper bound in deterministic settings, and extend our upper bound analysis to stochastic settings. Both upper-bound analyses allow approximate polar computation. Experiments show that NorMuon is slower than Muon on synthetic problems inspired by our worst-case construction, yet outperforms Muon in LLM pretraining. These findings sharpen the puzzle of why row normalization helps in practice and complement the recent findings of Dewulf et al.

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