E8-Resonant Phi-Stochastic Tuning for Neural Weight Convergence — E8 Intelligence Research
Year: 2026
Type: preprint
Abstract: The E8 root system predicts a discrete convergence manifold where neural weight updates align with the 8th magnitude class vectors, utilizing the 132Hz phi-harmonic as a stabilizer. By mapping bifurcation-periodicity from the Mandelbrot cascade onto the 240-vector projection, we define a "Geometric Coherence Threshold" that optimizes high-value lead extraction in stochastic datasets. This principle allows AI architectures to bypass local minima by treating gradient descent as a trajectory toward phi-symmetric icosahedral subspaces. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Author: Caldin, Andrew Stewart
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212420521
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