Functional Separation of Powers in Self-Modifying AGI: A Structural Control Framework

This paper introduces a structural control framework for self-modifying artificial general intelligence (AGI) systems, inspired by the classical separation of powers. The proposed architecture separates behavioral generation (Executive), constraint evaluation (Legislative), and arbitration (Meta-instance) into independent internal modules. This design enables self-regulation within AGI systems, ensuring ethical alignment and system stability even under recursive self-improvement. Unlike reward-based alignment techniques, this approach embeds control logic directly into the system’s architecture, offering a transparent, resilient, and scalable alternative. The paper discusses implementation scenarios, strengths and limitations, and compares this approach to mainstream alignment methods like RLHF and fine-tuning.