Edge-Centric Computation and Empirical Robustness of Dodecaflake Architectures

Edge-Centric Computation and Empirical Robustness of Dodecaflake Architectures.

This work introduces original architectures and a model framework developed by G.J.Y. Desvaux at Hope 'n Mind SASU.

Proprietary Contributions

• The Edge-Centric Hypothesis - a novel framework establishing that information in neural networks may resides in edge topologies, not in nodes

• The Hexaflake Architecture - an original edge-centric neural network design using linear chains of capture cells with bidirectional contextual integration

• The Dodecaflake Architecture - an original twelve-facet tensor interference geometry with non-parametric Hadamard interference at the topological center

• The Tensor Interference Mechanism - a novel computation method for combining counter-propagating signals within edge structures

• A methodology proving node pseudo-neutrality and topological protection in neural networks

Experimental Evidence

Systematic ablation experiments demonstrate that 75% of propagation pathways can be destroyed with zero performance loss, while the central interference mechanism remains irreplaceable - establishing a new class of intrinsically fault-tolerant neural architectures.

Citation and Usage

Any academic work replicating, adapting, or building upon these architectures, mechanisms, or theoretical frameworks must cite this foundational deposit. Commercial exploitation requires explicit written agreement with Hope 'n Mind SASU