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

aimingoo的专栏
aimingoo的专栏
宝玉的分享
宝玉的分享
博客园 - 【当耐特】
博客园 - 司徒正美
L
LangChain Blog
有赞技术团队
有赞技术团队
大猫的无限游戏
大猫的无限游戏
Stack Overflow Blog
Stack Overflow Blog
Engineering at Meta
Engineering at Meta
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
I
InfoQ
博客园 - 叶小钗
H
Hackread – Cybersecurity News, Data Breaches, AI and More
J
Java Code Geeks
月光博客
月光博客
量子位
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
博客园_首页
Last Week in AI
Last Week in AI
人人都是产品经理
人人都是产品经理
Google DeepMind News
Google DeepMind News
云风的 BLOG
云风的 BLOG
D
DataBreaches.Net

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
Automated Heuristic Design for Network Operations
Reza Namvar, José Gallego, Jose A. Ayala-Romero, Livia Elena Cha · 2026-05-27 · via math updates on arXiv.org

Network operation relies on heuristics to solve many tasks rapidly and efficiently across the protocol stack. These heuristics are the result of thorough human-driven design rooted in expert knowledge of the target system and problem. Recently, approaches powered by Artificial Intelligence have shown promising results in devising solutions that outperform long-established heuristics in classical problems. We explore the possibility of applying such Automated Heuristic Design (AHD) frameworks to network environments by (i) discussing the general integration of AHD with network operation and the associated challenges, as well as (ii) proposing a practical implementation of AHD for a specific networking task, i.e., 5G decoding. Initial results show how modern AHD tools can devise heuristics for Low-Density Parity Check decoding on par with state-of-the-art solutions implemented in production systems.