arXiv Machine Learning August 4, 2026 By F\'elix Marcoccia, Victor Fagoo, Gilles Monzat, C\'edric Adjih, Thomas Watteyne, Paul M\"uhlethaler

NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies

Read the original on arXiv Machine Learning →

arXiv:2410. 08238v2 Announce Type: replace-cross Abstract: We introduce NetDiff, a node-conditioned denoising diffusion model that generates directional link topologies and a two-slot transmit/receive parity for mobile ad hoc networks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 6

FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networks

arXiv:2608. 04050v1 Announce Type: cross Abstract: Wi-Fi networks are moving beyond random channel access toward tightly coordinated operation across access points (APs), a shift reflected in Wi-Fi 8's multi-AP coordination (MAPC).

By Maksymilian Wojnar, Krzysztof Rusek, Katarzyna Kosek-Szott, Szymon Szott

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CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge

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By Zhiheng Guo, Zhaoyang Liu, Zihan Cen, Chenyuan Feng, Xinghua Sun, Xiang Chen, Tony Q. S. Quek, Xijun Wang

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arXiv Machine Learning

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Graph Signal Diffusion Models for Wireless Resource Allocation

arXiv:2604. 05175v2 Announce Type: replace-cross Abstract: We consider constrained ergodic resource optimization in wireless networks with graph-structured interference.

By Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti, Alejandro Ribeiro

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arXiv Machine Learning

Jun 3

RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction

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By Zixuan Guo, Xiucheng Wang, Nan Cheng

diffusion benchmarks

arXiv AI

Jun 2

Physics-Guided Geometric Diffusion for Macro Placement Generation

arXiv:2605. 16451v2 Announce Type: replace-cross Abstract: Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance.

By Jongho Yoon, Jinsung Jeon, Seokhyeong Kang

llms diffusion benchmarks

arXiv AI

Jun 30

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv:2606. 28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data.

By Samuele Sabella, Chiara Boldrini, Lorenzo Valerio, Marco Conti, Andrea Passarella

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FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networks

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge

Graph Signal Diffusion Models for Wireless Resource Allocation

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Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints