Conceptual Framework for a SelfEvolving Machine: Towards Adaptive Efficiency Beyond Human Design Limits

This research introduces a theoretical framework for a Self-Evolving Machine, a system capable of continuously improving both its cognitive and physical architecture. By shifting the idea of efficiency from fixed thermodynamic limits to dynamic Adaptive Efficiency, the paper explores how machines can incrementally enhance their own problem-solving abilities through self-modifying algorithms and reconfigurable physical structures. Integrating concepts from Darwin-Gödel self-improving AI, modular robotics, and self-healing materials, the study outlines how such a system could co-evolve in mind and body while remaining aligned with human-defined safety constraints. The work aims to inspire long-term pathways toward safer, more adaptive autonomous systems.