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

美团技术团队
T
The Blog of Author Tim Ferriss
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
Engineering at Meta
Engineering at Meta
量子位
I
InfoQ
Jina AI
Jina AI
Microsoft Security Blog
Microsoft Security Blog
H
Help Net Security
H
Hackread – Cybersecurity News, Data Breaches, AI and More
G
Google Developers Blog
J
Java Code Geeks
Recent Announcements
Recent Announcements
aimingoo的专栏
aimingoo的专栏
小众软件
小众软件
V
V2EX
腾讯CDC
P
Proofpoint News Feed
A
About on SuperTechFans
爱范儿
爱范儿
U
Unit 42
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Last Week in AI
Last Week in AI

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
Discrepancy for Random Linear Codes
[Submitted on 23 Jun 2026] · 2026-06-24 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We prove that random linear codes have nearly optimal discrepancy properties in a broad range of regimes. Our main results are two general theorems: one controlling all translates of a fixed test, and another controlling large families of Fourier-pseudorandom tests. Two motivating applications follow.
First, random linear codes match unstructured random codes for list-decoding from errors above capacity. If $C\subseteq\mathbb F_q^n$ is a random linear code of rate $1-\frac1n\log_q |B_\rho|+\epsilon$, where $B_\rho$ is a radius-$\rho$ Hamming ball, then with high probability $$ |C\cap B|=(1\pm o(1))\frac{|C||B|}{q^n} $$ simultaneously for all radius-$\rho$ Hamming balls $B\subseteq\mathbb F_q^n$. This extends the classical result that such codes have covering radius at most $\rho n$ whp (Blinovsky, 1987).
Second, over prime fields, random linear codes match unstructured random codes for zero-error list-recovery above capacity. For prime $q>2$ and $2\le \ell\le q-1$, a random linear code of rate $1-\log_q\ell+\epsilon$ satisfies, with high probability, $$ |C\cap S|=(1\pm o(1))\frac{|C|\ell^n}{q^n} $$ simultaneously for all rectangles $S=S_1\times\cdots\times S_n$ with $|S_i|=\ell$. As a consequence, there are abundant $n$-party linear ramp secret sharing schemes over $\mathbb F_q$ with privacy threshold about $n/(2\log q)$ and reconstruction threshold about $5n/(2\log q)$, resilient to balanced local leakage; prior existence results required thresholds above $n/2$ even in this case.
The translate result, hence the list-decoding application, holds over arbitrary finite fields, even growing with $n$. The list-recovery and leakage applications hold over prime fields under moderate growth, e.g. $q\le n^{1/5-o(1)}$. The proofs use a refined second-moment analysis tracking intersection sizes as random generators are added to $C$.

Submission history

From: Jonathan Mosheiff [view email]
[v1] Tue, 23 Jun 2026 12:01:54 UTC (53 KB)