Universal Balance–Feedback AGI Architecture (UBF-AGI): A Causal, Entropy-Regulated and Integrity-Constrained Framework for General Intelligence Systems

ABSTRACT We present a unified AGI architecture grounded in four system-level laws drawn from the Universal Balance–Feedback Framework (UBFF): causal consequence propagation (Law of Karma), equilibrium regulation (Universal Law of Balance), recursive network feedback (Universal Feedback Loop Mechanism), and structural integrity enforcement (Universal Interconnected Nodes). We formalize a novel class of intelligence systems wherein learning is not solely reward-optimized but is jointly constrained by entropy stability, causal accountability, and global system coherence. The resulting framework — UBF-AGI — integrates transformer-based architectures, constrained reinforcement learning, and multi-agent graph systems into a single regulated causal network. We provide precise variable definitions for all mathematical constructs, a differentiable soft integrity constraint, a Lyapunov stability sketch under stated assumptions, and an expanded citation base covering active inference, causal inference, and safe AI alignment literature.