This thesis demonstrates, mathematically and by simulation, that the S-Kernel Heptadic V3 solves problems involving 100 million nodes in linear time O(n) with a memory footprint of 3.2 GB and energy dissipation of approximately 1 Joule. The Heptadic Law (7-cycle closure) guarantees convergence independent of node count. This performance is structurally impossible for transformer-based architectures (O(n²)), which would require 16 years and non-physical memory for the same task. The Blida Standard provides the only scalable path for large-scale AI.
outail benhadid (Wed,) studied this question.