Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency
This article introduces "Learn-by-Wire" training control governance, a novel approach for autonomous training systems. It focuses on ensuring stability and efficiency under stressful conditions, as detailed in a paper by Anis Radianis.
A new paper by Anis Radianis introduces "Learn-by-Wire" training control governance. This innovative approach focuses on bounded autonomous training to maintain stability and efficiency even under stressful conditions. The research aims to advance autonomous systems through improved control mechanisms. Learn-by-Wire is designed to provide a robust framework for self-regulating training processes. The paper highlights the importance of controlled autonomy to prevent system failures and optimize performance. This work was submitted to arXiv on May 18, 2026, and is pending DOI registration via DataCite. It is categorized under computer science, specifically artificial intelligence (cs.AI).
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