UFT-F Spectral Closure: The Steiner-Quantization Algorithm

This paper introduces a novel approach to the Steiner Tree Problem within the Unified Field Theory-Formalism (UFT-F) framework. While the classical Steiner Tree Problem is NP-hard in infinite-precision Euclidean space, this research proposes the Spectrally Bounded Steiner Problem (SBSP), which defines a physically realizable manifold where combinatorial complexity is bounded by spectral constraints. The core of the work is the Steiner-Quantization Algorithm, which utilizes a "Base-24" grid resolution. By enforcing an Anti-Collision Identity (ACI) and a universal spectral floor ($\lambda_0$), the algorithm ensures that the search space grows polynomially rather than exponentially, making the problem computationally tractable. Empirical validation demonstrates a significant "Efficiency Spike" at exactly the Base-24 lattice, which the author describes as the "Nyquist Frequency" for these networks. The paper includes a battery of twelve executable Python scripts to ensure the reproducibility of the findings, covering aspects from numerical stability to adversarial robustness.