Dimensional Memory

Dimensional Memory is the capacity of higher dimensional layers to retain the computational history of the layers beneath them while accumulating new information specific to their own architecture. The book names it as one of the properties of the Hierarchical Dimensional Architecture, alongside tension propagation and causal nesting: each new axis joins a memory-retaining network. As higher layers emerge they keep historical data and add information unique to their complexity, producing a recursive memory lattice in which dimensional history and innovation coexist. Its law is the Memory Evolution Equation, M_n(t) = M_{n-1}(t) × η𝓜(t) + I_{▷,n}(t) × α⫷(t): memory at level n is the retained fraction of the memory below plus a fraction of newly acquired information, measured in bits. The Memory Capacity Function limits how much can be stored at each depth. It connects to Memory Evolution Equation, Memory Capacity Function, Curvature Retention, Field Memory, Causal Nesting and Tension Propagation.