Artificial intelligence (AI) and reinforcement learning (RL) have shown significant promise in wireless systems, enabling dynamic spectrum allocation, traffic management, and large-scale Internet of Things (IoT) coordination. However, their deployment in mission-critical applications introduces the risk of unsafe emergent behaviors, such as UAV collisions, denial-of-service events, or instability in vehicular networks. Existing safety mechanisms are predominantly reactive, relying on anomaly detection or fallback controllers that intervene only after unsafe actions occur, which cannot guarantee reliability in ultra-reliable low-latency communication (URLLC) settings. In this work, we propose a proactive safety-constrained RL framework that integrates proof-carrying control (PCC) with empowerment-budgeted (EB) enforcement. Each agent action is verified through lightweight mathematical certificates to ensure compliance with interference constraints, while empowerment budgets regulate the frequency of safety overrides to balance safety and autonomy. We implement this framework on a wireless uplink scheduling task using Proximal Policy Optimization (PPO). Simulation results demonstrate that the proposed PCC+EB controller eliminates unsafe transmissions while preserving system throughput and predictable autonomy. Compared with unconstrained and reactive baselines, our method achieves provable safety guarantees with minimal performance degradation. These results highlight the potential of proactive safety constrained RL to enable trustworthy wireless autonomy in future 6G networks.
From Assistance to Agency: Rethinking Autonomy and Control in CI/CD Pipelines
Faculty of Information University of Toronto Toronto Ontario Canada（信息学院多伦多大学多伦多安大略加拿大）；Department of Computer Science Trent University Peterborough Ontario Canada（计算机科学系特伦特大学彼得伯格安大略加拿大）；University of Toronto（多伦多大学）；Trent University（特伦特大学）
Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance
Samsung Research（三星研究院）；The Royal Melbourne Institute of Technology（皇家墨尔本理工大学）；Dublin City University（都柏林城市大学）；Munster Technological University（芒斯特理工大学）
Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety
School of Computing and Communications（计算与通信学院）；University of Cambridge（剑桥大学）；School of Software（软件学院）；Nanjing University of Information Science and Technology（南京信息科技大學）；TU Wien（维也纳技术大学）；ICREA
