The Inversion Error: Why Safe AGI Requires an Enactive Floor and State-Space Reversibility

This article examines fundamental structural limitations in current AI systems that cause hallucination and corrigibility problems, arguing that scaling alone cannot bridge the gap between how AI systems operate and what's needed for safe AGI.

The author proposes that safe AGI requires an "enactive floor" and state-space reversibility—architectural changes that ground AI systems in verifiable reality rather than relying solely on scaling larger models.