The Self-Evolving Machine: A Conceptual Framework for Adaptive Efficiency and Open-Ended Intelligence

This paper presents a conceptual framework for the Self-Evolving Machine (SEM), a theoretical system designed to achieve continuous, self-directed evolution in both its cognitive and physical architecture. The central thesis is that the traditional engineering constraint of efficiency, defined by the First and Second Laws of Thermodynamics [1], can be transcended by reframing the concept as Adaptive Efficiency ( ). We formally define as the rate of improvement in a system’s problem-solving capacity over time, providing a quantitative metric for open-ended intelligence. The framework synthesizes principles from complexity theory, evolutionary computation, and advanced robotics. Specifically, the paper details the cognitive mechanism via the Darwin-Gödel Machine (DGM) paradigm [2] and the physical mechanism via Modular Self-Reconfiguring Robotics (MSRR) [3] and selfhealing materials. By integrating these components into a closed-loop system, the SEM is posited as a path toward artificial general intelligence capable of accelerating scientific discovery and solving currently intractable global challenges. The paper concludes with a critical discussion of the profound ethical and safety implications inherent in systems with unbounded self-improvement capabilities.