Emergent Relational Time via Morphological Resistance and Porous Boundary Adaptation in Configuration Space

We propose a background-independent framework in which ”time” is not a fundamental dimension of spacetime, but an emergent, relational metric generated by the traversal of a configuration space (C). Trajectories across C are governed by a Morphological Resistance functional R(q), which quan- tifies structural and logical incoherence between static states. Furthermore, we model the boundary conditions of the system (∂C) as an adaptive, porous structure (”morphological chip”) exhibiting hysteresis—where particle transits deform boundary channels, minimizing local resistance for sub- sequent states. To validate this framework, we present a falsifiable experimental protocol utilizing a discrete N-body Python ”toy model” a and Graph Neural Network (GNN) path optimization.