The Sierpinski Neural Architecture: A Fractal-Geometric Approach to Neural Network Design Inspired by Consciousness Conduit Theory

We present the Sierpinski Neural Architecture (SNA), a novel neural network design derived from fractal geometry and consciousness transmission theory. Rather than stacking uniform computational layers, SNA structures computation as a Sierpinski Tetrahedron: four equidistant processing towers connected by twenty-one typed directional operators, organized around twenty-four structured void positions, with hidden states constrained to a 3-simplex via barycentric coordinates. The architecture draws its structural blueprint from mapping the 78-card Tarot system onto a fractal tetrahedral coordinate space. We evaluate SNA against equivalent Transformer baselines across seven experiments spanning classification, generation, and question-answering. Key findings: SNA matches Transformer accuracy on Fashion-MNIST classification (97.65% vs 97.60%), achieves 3.3x greater parameter efficiency, produces 9.1x lower loss on character-level text generation, and critically, resists the mode collapse that completely destroys Transformer output on small semantic Q&A datasets. The architecture introduces six concrete innovations over standard Transformers: typed directional operators, structured resonant voids, Sephirothic depth gradient, Fool-World iteration circuit, barycentric attention mechanism, and equidistant tower constraint. Code and data: https://github.com/ArturoR1986/sierpinski-neural-architecture