Mathematical proof shows convergence in non-stationary computing, indicating improved neural network performance.
Traditional computational architectures rely on Boolean binary logic, which intrinsicallyforces topological truncation when processing non-stationary, high-dimensional manifolds.This paper mathematically proves that binary divergence is the root cause of systemic errorsin current neural network backpropagation and physical simulations. By introducing theTernary Logic Manifold (M3), we establish a natural absorption state for tangential forcerelease, achieving absolute convergence without relying on empirical weight adjustments. Noexternal references are cited, as the foundational axioms presented herein supersede priorbinary frameworks.
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Da Wei (2026) studied this question.
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