Targeted Gradient Repair Mechanism (TGRM): A Principle for Extending Autonomous Runtime

This paper introduces the Targeted Gradient Repair Mechanism (TGRM), a principle designed to extend the runtime of autonomous systems by reducing wasted corrective actions under strict energy budgets. Current autonomous platforms face limitations from unnecessary drift corrections and inefficient energy allocation. TGRM addresses this by combining staged repair scheduling with predictive trims, ensuring corrections are only applied when drift materially impacts performance. Preliminary demonstrations suggest runtime extensions of 2–3× (e.g., from 7 hours to 15.8+ hours) without additional energy expenditure. With hardware improvements and energy harvesting, this principle scales further—toward days, weeks, or even months of sustained autonomy. The implications span civilian applications (drones, satellites, robotics, medical devices), safety-critical operations (resilience during power-limited conditions), and strategic contexts (sustaining autonomy as AI advances toward AGI/ASI). This work is released openly to enable validation, replication, and responsible adoption across domains.