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

Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 三生石上(FineUI控件)
雷峰网
雷峰网
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
博客园 - 叶小钗
The Cloudflare Blog
T
Tailwind CSS Blog
Hugging Face - Blog
Hugging Face - Blog
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
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
小众软件
小众软件
罗磊的独立博客
酷 壳 – CoolShell
酷 壳 – CoolShell
大猫的无限游戏
大猫的无限游戏
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
美团技术团队
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 聂微东
量子位
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
宝玉的分享
宝玉的分享

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Stacked Intelligent Metasurface-Aided Wave-Domain Signal ...
[Submitted on 22 Jan 2026 (v1), last revised 27 Jul 2026 (this v · 2026-01-22 · via cs.IT updates on arXiv.org

View PDF HTML (experimental)

Abstract:Artificial neural networks possess remarkable capabilities for abstract feature extraction, while electromagnetic computing leverages wave propagation to execute complex mathematical operations. Concurrently, metasurfaces engineered from subwavelength meta-atoms offer unprecedented control over electromagnetic wavefronts. Synthesizing these three cutting-edge fields has sparked significant interest in developing electromagnetic neural networks via stacked intelligent metasurface (SIM) technology, which aims to execute diverse signal processing tasks directly within the wave domain. By enabling direct processing of information-carrying electromagnetic waves, SIMs offer a promising paradigm for high-speed, massively parallel, and low-power signal processing. This article provides a comprehensive overview of SIM technology, beginning with its evolutionary trajectory. We then delve into its theoretical foundations and examine state-of-the-art SIM hardware prototypes. Furthermore, we analyze the optimization and training strategies devised to configure SIM functionalities from two distinct perspectives. Additionally, the diverse applications of SIM technology across the communication, sensing, and computing domains are explored, supported by experimental evidence that highlights its ability to sustain multiple functions within a single device. Finally, we outline critical technical challenges to deploying SIMs in next-generation wireless networks and chart promising research directions to fully unlock their transformative potential.

Submission history

From: Jiancheng An [view email]
[v1] Thu, 22 Jan 2026 14:58:43 UTC (11,698 KB)
[v2] Mon, 27 Jul 2026 09:03:23 UTC (14,029 KB)