Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic
The School of Engineering & Physical Sciences（工程与物理科学学院）；SDAIA-KFUPM Joint Research Centre for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）；Heriot-Watt University（赫瑞-沃森大学）；King Fahd University of Petroleum and Minerals（国王法赫德石油大学）
Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hidden by idealised quantisation models. In particular, two's-complement overflow wrapping can corrupt hidden activations by changing both their magnitude and sign, leading to unstable numerical error propagation and severe accuracy degradation. This paper proposes a Lyapunov-stabilised quantisation framework for low-precision neural networks operating under hardware-style wrapping arithmetic. The hidden-state energy is monitored through a layerwise Lyapunov function, and a monotone projection is applied to enforce bounded and non-increasing state evolution across depth. The method is evaluated on MNIST using a compact patch-based transformer under post-training quantisation and quantisation-aware training with fixed-point bit-widths from 4 to 16 bits. Monte Carlo results show that unconstrained wrapped quantisation-aware training collapses to near-chance accuracy across 6-16 bits, with activation overflow rates exceeding 11%. In contrast, the proposed monotone Lyapunov projection suppresses activation overflow to below 0.012% and restores stable low-precision learning, achieving 86.55% accuracy at 12 bits. These results demonstrate that Lyapunov-based state control can act as a hardware-aware stabilisation mechanism for reliable fixed-point neural inference and training.
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
Delft University of Technology（代尔夫特理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；Huawei Technologies（华为技术有限公司）；University College London（伦敦大学学院）；University of Manchester（曼彻斯特大学）
Lyapunov-Aware Quantum-Inspired Reinforcement Learning for Continuous-Time Vehicle Control: A Feasibility Study
School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand（信息、计算机与通信技术学院，Sirindhorn国际技术学院，泰国朱拉隆梭大学，Pathum Thani）
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
Department of Mechanical Engineering, Iowa State University（爱荷华州立大学机械工程系）；Department of Weapons, Robotics, and Control Engineering, United States Naval Academy（美国海军学院武器、机器人与控制工程系）；Navy Center for Applied Research in Artificial Intelligence (NCARAI), U.S. Naval Research Laboratory（美国海军人工智能应用研究中心（NCARAI））
MIRPALab, Department of Engineering, University of Palermo（MIRPAL实验室、工程系、巴勒莫大学）；Laboratoire LTI, Université de Picardie Jules Verne（LTI实验室、皮卡第大学朱利斯·韦尔纳大学）；University of Palermo（巴勒莫大学）
