Scaling Emergence: From 3D Human Cognition to N-Dimensional AGI with Tensormatics

This companion paper to Tensormatics: A New Algebra of Emergent Intelligence explores the scalable architecture of emergent cognition beyond human cognitive limits. By extending the core principle of triadic convergence (3D fusion) to N-dimensional input spaces, this work presents a clear roadmap from current AI systems to hyper-dimensional artificial general intelligence (AGI). Using geometric analogies from hypercubes and real-world examples across biology, perception, and neural processing, we formalize how recursive fusion (⊕ operator) enables rich, nonlinear synthesis across orthogonal cognitive domains. We introduce TensorConverge Neurons (TCNs), TensorConverge Neural Networks (TNNs), and TensorGPT as stages in the progression toward synthetic cognition. The paper also highlights computational constraints—including hardware, software, and energy barriers—and proposes triadic systems as the most feasible near-term entry point. With pseudocode, implementation examples, and a call to action, this work positions Tensormatics as both visionary and practical, laying the foundation for the emergence of structured AGI through dimensional convergence.