EvoSignal is an LLM‑guided evolutionary framework that treats traffic signal control as a modular program design problem, separating traffic feature extraction, local phase prioritization, and optional network‑based priority adjustment. Starting from established strategies, it iteratively improves programs using congestion and signal operation feedback, ultimately producing control rules that operate without online LLM inference. In simulations on two real‑world road networks, the best EvoSignal program reduced waiting time by 16.8–49.2% compared to 20 conventional, reinforcement‑learning, and LLM‑based baselines, and a travel‑time/queue‑length prioritizing program consistently outperformed all baselines across multiple scenarios.
By Leizhen Wang, Peibo Duan, Zhenlin Qin, Yancheng Ling, Jian Xu, Yue Wang, Hao Wang, Zhenliang Ma
arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.
By Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Chongbo Sun, Xuefeng Tao, Wei Yang, Le Zhang, Wenwu Zhu, Peng Cui, Zhi Wang
arXiv:2509. 20008v2 Announce Type: replace Abstract: Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation.
By Raphael Simon, Pieter Libin, Wim Mees
arXiv:2603. 19864v2 Announce Type: replace Abstract: Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inherently partially observable and features large action spaces.
By Raphael Simon, Jos\'e Carrasquel, Wim Mees, Pieter Libin
The paper presents a method for creating Open RAN (O‑RAN) slicing xApps that are both adaptive and fully inspectable. By using a large language model (LLM) to evolve compact Python programs, the authors generate slicing controllers whose decision logic remains readable and editable after optimization. In experiments on the NSF POWDER 5G testbed, the evolved controllers improved best‑effort throughput by 44.5% and reduced SLA misses from 79.9% to 2.2%, while evolutionary search outperformed independent prompting in trace‑driven simulations.
By Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari
arXiv:2608. 13575v1 Announce Type: cross Abstract: Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications.
By Igor Cherepanov, David Sessler, Alex Ulmer, Felix Wagner, Throsten May, J\"orn Kohlhammer