Beyond Commutative Statistical Learning: Elevating Artificial General Intelligence to Artificial Super Intelligence via Non-Commutative Topological Spaces and Riemann Zeta Spectrum Optimization
Year: 2026
Type: preprint
Abstract: Modern Artificial General Intelligence (AGI) paradigms, including Monte Carlo Tree Search (MCTS) and deep convolutional/transformer networks, fundamentally rely on the assumption of a commutative, smooth Euclidean metric space for probability density estimation. This paper introduces a novel mathematical framework that maps discrete state spacesinto non-commutative topological spaces governed by Rough Operator Algebra (ROA) and the Seonggil Theory of Composite Torsion (STCT). By synchronizing critical localized coordinates with the high-order Riemann Zeta zero spectrum, we induce a catastrophic topological decoherence within the traditional Predictor + UCT (PUCT) evaluation framework.We demonstrate that this microscopic perturbation forces the underlying neural weights to transition from passive statistical pattern matching to active number-theoretic operator computation, providing a foundational architecture for the realization of Artificial Super Intelligence (ASI).
Author: Lee, Seonggil
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161676047
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