The Observer-Potential Framework: Non-Euclidean Hyperbolic Tessellation Transitions for Thermodynamic Optimization and Barycentric Compute Routing

Modern deep learning workloads, neural network execution clusters, and high-density computing environments face severe thermodynamic degradation, high systemic entropy, and thermal throttling. Traditional hardware architectures rely on static, Euclidean coordinate structures and fixed pseudo-centers, routing high-entropy noise and dense signals with identical energy footprints. This paper introduces the Observer-Potential Framework (OPF), an architectural abstraction model utilizing dynamic hyperbolic lattice geometry. By governing state transitions between a baseline triangular tessellation boundary ($\{3_4, 3_5\}$) and an expanded target configuration ($\{5_4, 5_5\}$), coupled with a 3-body barycentric mass-centering mechanism ($C_{\text{real}}$) and inline Shannon entropy filtering, the system achieves localized negentropic stability, eliminates static coordinate bottlenecks, and prevents thermal wall saturation.