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

Y
Y Combinator Blog
B
Blog
S
SegmentFault 最新的问题
Vercel News
Vercel News
博客园 - 聂微东
宝玉的分享
宝玉的分享
C
Check Point Blog
有赞技术团队
有赞技术团队
IT之家
IT之家
V
V2EX
爱范儿
爱范儿
GbyAI
GbyAI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
P
Proofpoint News Feed
博客园 - 司徒正美
博客园_首页
Last Week in AI
Last Week in AI
博客园 - 叶小钗
量子位
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
F
Fortinet All Blogs
腾讯CDC
J
Java Code Geeks

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
On the Unification of Deterministic and Stochastic Electr...
[Submitted on 10 Aug 2025 (v1), last revised 8 Jul 2026 (this ve · 2025-08-10 · via cs.IT updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper unifies deterministic and stochastic Electromagnetic Information Theory (EIT) through symplectic geometry. For spatially incoherent sources, both formulations yield identical eigenvalues and spatial Number of Degrees of Freedom (NDF). In the asymptotic regime and in the absence of losses, this equivalence is shown to be a structural necessity: the radiometric étendue, the Hamiltonian phase-space volume, and the NDF are the same symplectic invariant of the source--observer configuration. Liouville's theorem guarantees conservation of the NDF under lossless propagation, while Gromov's Non-Squeezing Theorem establishes a minimum phase-space cell, setting a fundamental geometric bound on resolving power. The physical manifestation of this symplectic structure is the formation of \textit{Spatial Information Flows} (SIFs), defined operationally as the spatial loci along which the spatial coherence, equivalently the mutual information, decays at the minimum possible rate. Spatial information in electromagnetic fields is therefore governed by the geometry of the source--observer configuration, providing the foundation for a geometric theory of electromagnetic information.

Submission history

From: Migliore Marco Donald Prof. [view email]
[v1] Sun, 10 Aug 2025 13:04:38 UTC (584 KB)
[v2] Fri, 19 Sep 2025 13:25:02 UTC (585 KB)
[v3] Sat, 4 Oct 2025 10:19:49 UTC (587 KB)
[v4] Thu, 16 Apr 2026 06:58:29 UTC (738 KB)
[v5] Wed, 8 Jul 2026 06:48:44 UTC (739 KB)