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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.