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Learning Changes the World and Then What Was Learned has Changed: Training and Deploying Neural Networks in the Control Loop

arXiv · AI, language, vision and robotics · article · Oct 15, 2026 · UTC

Neural networks offer critical adaptability for controlling complex systems where nominal modeling proves intractable. However, when offline-trained neural networks are deployed within closed-loop control systems, their actions fundamentally alter the state distribution upon which they were trained. This paper introduces a unified taxonomy to categorize these sequential failure modes: the initial Closed-Loop Identification Error, the deployment-induced Action Error, and the continuous Tracking Error. Through discrete-time sensitivity analysis, this work formalizes how offline-learned parameter

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