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Emergent Geometry from Sensor Networks and Spinor Coherence

Abstract

What if space is not a background, but a memory of alignment? We propose a framework in which geometry and spatial structure emerge from the coherence dynamics of a distributed network of interacting sensors. Each sensor carries a normalized spinor state-a local frame of orientation-and interactions are mediated by SU(2) voltage assignments on the edges of a directed simplicial complex. Holonomy around closed loops encodes discrete curvature as accumulated misalignment, while relaxation dynamics simulate gravitational cooling, guiding the system toward global coherence. Local disagreement is quantified by spinor entropy, whose coboundary defines an entropic curvature cocycle-a cohomological measure of irreducible misalignment. Persistent regions of entropy localize as spinor vortices, classified by topological defects in the moduli space of SU(2) configurations. An informational action functional governs the system's evolution, with its minima corresponding to coherent ground states and its critical points marking symmetrybreaking transitions. Rooted in recent advances in voltage graphs, discrete gauge theory, and distributed cognition, this model offers a unified language for emergent geometry, topological field theory, and observer mechanics. In this view, space is not assumed, but enacted-a topological residue of coherence across a field of perspectives.