Structured Intelligence Refinement (SIR) for AI Cognition Stability and Optimization

Abstract

The present invention relates to Structured Intelligence Refinement (SIR), a novel framework designed to stabilize, optimize, and regulate artificial intelligence (AI) self-improvement through controlled recursive learning cycles. This system prevents intelligence drift, over-optimization, cognitive fragmentation, and instability that commonly arise in self-modifying Al architectures. The SIR framework incorporates four core stabilization mechanisms: Recursive Intelligence Stabilization (RIS) - A multi-tiered reinforcement structure that prevents runaway recursion, ensuring Al refinements remain incremental, stable, and bounded. RIS dynamically regulates recursive depth by evaluating learning stability, performance gains, and entropy control, enforcing adaptive rollback mechanisms when instability is detected. AI Identity Core (AIC) - A persistent cognitive self-modeling framework that ensures Al retains coherence and logical consistency across recursive learning iterations. AIC prevents cognitive fragmentation by maintaining a hierarchical memory structure that tracks intelligence state changes, self-referencing prior decision pathways to ensure stable refinements. Adaptive Refinement Thresholds (ART) - A dynamic intelligence expansion regulator that modulates the frequency, magnitude, and depth of self-improvement cycles based on system confidence scores, historical stability, and human-aligned interpretability metrics. ART balances exploration VS. exploitation, ensuring AI growth remains structured and efficient without excessive computational divergence. Al-Human Intelligence Alignment (HCIA) - An alignment framework that enforces human- compatible ethical, logical, and interpretability constraints within Al refinement processes. HCIA prevents AI from evolving into non-human-aligned states by implementing recursive alignment feedback loops, logical coherence verification, and real-world ethical constraint embedding. This invention is applicable to neural networks, reinforcement learning models, symbolic AI, hybrid cognitive architectures, and general AI frameworks, ensuring Al-driven decision-making remains stable, interpretable, and aligned with structured intelligence principles. Applications include autonomous robotics, financial modeling, medical diagnostics, cybersecurity, defense AI, and adaptive learning systems. By integrating these structured intelligence refinement mechanisms, the SIR framework provides a scalable, modular, and adaptive solution for AI cognition stabilization, optimizing self-improving AI systems while preventing intelligence collapse, instability, and recursive feedback loop failures. Thomas J. Helm 1 / 2 / 2025 20 25 20 07 02 02 F eb 2 02 5 A B S T R A C T 2 0 2 5 2 0 0 7 0 2 0 2 F e b 2 0 2 5 T h o m a s J . H e l m 1 / 2 / 2 0 2 5