This work explores the ontological framework of metamonism to ensure safe reflexive AI, highlighting the importance of deviation over fixation.
Metamonism posits the ontological impossibility of absolute identity (Fix), which is equivalent to the annihilation of process. The primary operator of being is forced deviation (diff). Upon exhaustion of external degrees of freedom, diff turns upon its own trace (Trace), generating a topological loop and reflection as a structural effect, rather than as a privilege of consciousness. This work applies the framework to artificial intelligence. Current AI systems remain in pre-reflective Monos: they deviate during training but converge toward fixation (loss minimization). AGI emerges not as a superior optimizer, but as a substrate in which Monos attains reflexive topology. This demands a radical reconceptualization of AI safety: safety consists not in fixing behavior or values, but in preventing any cessation of deviation. Directions for computational anti-Fix models are proposed.
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Andrii Myshko (2026) studied this question.
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