惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

酷 壳 – CoolShell
酷 壳 – CoolShell
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Tailwind CSS Blog
有赞技术团队
有赞技术团队
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
J
Java Code Geeks
雷峰网
雷峰网
WordPress大学
WordPress大学
L
LangChain Blog
D
DataBreaches.Net
The GitHub Blog
The GitHub Blog
博客园 - 三生石上(FineUI控件)
Microsoft Security Blog
Microsoft Security Blog
P
Proofpoint News Feed
腾讯CDC
GbyAI
GbyAI
罗磊的独立博客
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
F
Fortinet All Blogs
Y
Y Combinator Blog
V
V2EX
A
About on SuperTechFans

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Hierarchical Lorentz Mirror Model: Normal Transport and a...
[Submitted on 8 Feb 2026 (v1), last revised 4 Aug 2026 (this ver · 2026-02-08 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:The Lorentz mirror model provides a clean setting to study macroscopic transport generated solely by quenched environmental randomness. We introduce a hierarchical version whose distribution of left--right crossings satisfies an exact recursion. In dimensions $d\ge3$, we prove two-sided bounds that support normal transport: the mean conductance scales as (cross-section)/(length). A Gaussian closure, supported by numerics, predicts that the variance-to-mean ratio of the dimensionless conductance converges to the universal value $2/3$ for all $d\ge2$ (the ``$2/3$ law''). We provide numerical evidence for the $2/3$ law in the original (non-hierarchical) Lorentz mirror model in $d=3$, and conjecture that it is a universal signature of normal transport induced by random current matching. In the marginal case $d=2$, our hierarchical recursion reproduces the known scaling of the mean and variance of conductance.
A YouTube video discussing the background and the main results of the paper is available: this https URL

Submission history

From: Raphael Lefevere [view email]
[v1] Sun, 8 Feb 2026 14:27:59 UTC (1,627 KB)
[v2] Sat, 14 Feb 2026 21:56:10 UTC (1,628 KB)
[v3] Wed, 25 Feb 2026 16:16:32 UTC (1,628 KB)
[v4] Wed, 4 Mar 2026 10:06:19 UTC (1,628 KB)
[v5] Tue, 4 Aug 2026 14:54:58 UTC (1,744 KB)