lyapunov

该论文提出 BRaVeS（DNRS）框架，通过不变锚点、深度感知注意力与 Lyapunov 共识机制约束智能体推理的自主性，防止认知漂移，并在工业控制仿真中验证了有限步收敛与零安全违规。

This paper presents a framework (CARE) that jointly learns control inputs and communication-efficient timing decisions under a pointwise Lyapunov safety shield, achieving higher inter-sample intervals than classical methods on inverted pendulum, cart-pole, and planar quadrotor systems.

The article presents a discovered spectral ratio between MLP and attention norms that predicts geometric stability in transformer models, with an optimal range of 0.5–2 to prevent rank collapse.