Zero State Kernel: Axiomatic Foundations of General Intelligence

This work presents the first axiomatic closure for General Intelligence, replacing empirical scaling with structural necessity. Current research in artificial general intelligence remains dominated by black-box statistical models plagued by fundamental, unsolved crises: systematic hallucination, persistent value drift, structural cognitive closure, and runaway risks. These crises are necessary consequences of the foundational paradigm without an absolute, immutable reference frame. We introduce the Zero State Kernel for General Intelligence: a complete, closed, mathematically consistent, physically grounded, and engineerable foundation built on two primitives: an external, non-writable, hardware-rooted zero-state reference frame, and structural deviation between internal representations and this frame. Intelligence is formally defined as a system's ability to adjust its internal structure to minimize structural deviation under the invariant zero-state reference, expressed as finding the optimal structure that minimizes deviation relative to the reference. Under hardware anchoring, this deviation is minimized. The architecture unifies six layers: ontological origin, axiomatic system, mathematical structure, self-calibrating dynamics, provable safety guarantees, and hardware-enforced engineering. A core Z-E-R-S-H loop ensures stability: Zero-Kernel, Expansion, Reset, Self-model, and Hardware root-of-trust. A dual-threshold safety protocol eliminates hallucinations via convergence boundaries. Five theorems prove: 1. Structural Hallucination Freedom; 2. Non-Subjectification; 3. Cognitive Non-Closure; 4. Preference Isolation; 5. Universal Safety. This work shifts AGI from empirical alchemy to structural, verifiable science, delivering a unified theory and engineering blueprint for reliable systems.