arXiv AI July 22, 2026 By Binggui Zhou, Bruno Clerckx

Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization

arXiv:2510. 15701v2 Announce Type: replace-cross Abstract: Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has recently been introduced to enable advanced control over electromagnetic waves to further increase the benefits of traditional RIS in enhancing signal quality and improving spectral and energy efficiency for next-generation wireless networks.

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

arXiv Machine Learning

Jul 16

Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

arXiv:2607. 14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing.

By Mustafa Emre G\"ursoy, Stefan Uhlich, Ryoga Matsuo, Ya\u{g}{\i}z Gen\c{c}er, Arun Venkitaraman, Chia-Yu Hsieh, Andrea Bonetti, Eisaku Ohbuchi, Lorenzo Servadei

reinforcement-learning benchmarks

arXiv AI

Jul 1

Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models

arXiv:2606. 31334v1 Announce Type: new Abstract: Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min fairness, and peak-to-average power ratio (PAPR)-constrained objectives.

By Ahmet Kaplan

llms diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 16

RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction

arXiv:2508. 16403v3 Announce Type: replace Abstract: Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear behavior and the high computational cost of traditional simulation tools.

By Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband

multimodal benchmarks

arXiv AI

Jul 16

Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach

arXiv:2607. 13160v1 Announce Type: cross Abstract: Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems.

By Tianyu Pang, Hongyu Li

reinforcement-learning

arXiv Machine Learning

1d ago

Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries

arXiv:2601. 03123v2 Announce Type: replace-cross Abstract: When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is, in principle, always possible using exact methods.

By Janani Gomathi, Alex Meiburg

arXiv Machine Learning

1d ago

DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design

arXiv:2608. 16080v1 Announce Type: new Abstract: Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve.

By Xinling Yu, Yixing Li, Ziyue Liu, Xin Ai, Zhiyu Zeng, Hai Li, Zheng Zhang

benchmarks

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Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models

RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction

Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach

Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries

DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design