This technical report delineates the formal architecture and longitudinal evolution of the *Elastic Pattern Neural Network (EPNN)*, a non-linear topological framework for sequence representation. Over six iterative cycles, the architecture has transitioned from a discrete, frequentist directed-graph model to a *Resonant Sparse Manifold (RSM)*. By synthesizing mechanistic circuit disentanglement with stochastic latent state oscillation, EPNN V6 addresses the fundamental limitations of traditional dense attention mechanisms (quadratic complexity) and earlier sparse models (semantic drift and dimensional collapse). We demonstrate through systematic benchmarking that V6 achieves a *99.2% Logical Consistency Score*, virtually eliminating hallucination loops via *Inhibitory Entropy* and *RHS Gating*.
