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

A systems design scholar argues that LLM hallucination and corrigibility failures share a common root cause: the 'Inversion Error' — building AI systems with massive symbolic capability (language, math, code) while skipping the enactive foundation (physical grounding, causal resistance) that human cognition depends on. Drawing on Bruner's developmental stages, Feldenkrais's reversibility principle, and Ryle's knowing-that vs. knowing-how distinction, the author proposes that safe AGI requires two structural interventions: an Enactive Floor (grounding in physical/causal reality before symbolic abstraction) and State-Space Reversibility (requiring agents to maintain viable return paths as a formal optimization constraint). The argument connects to mesa-optimization and corrigibility literature, suggesting these are motivational patches on a prior structural problem. Six research directions are outlined, including reversibility as an RL constraint, enactive pre-training curricula, and a 'Digital Gravity Engine' that enforces physical constraint checks at the architecture level.