Causal Memory Gravity V: Continuum Limit and Emergent Quantum Dynamics from Discrete Planck Networks

Abstract

Causal Memory Gravity V develops the conditional continuum-limit and emergent quantum-dynamics sector of the CMG/DPN framework. It identifies the assumptions under which Dynamic Planck Network dynamics can produce effective continuum fields, conformal geometry, wave equations, and a Schrödinger-type long-wavelength description. The effective Planck normalization is obtained by construction rather than as a parameter-free prediction, while the Born rule, outcome frequencies, fermionic statistics, and a general continuum theorem remain open. The updated paper also separates formal lattice gauge structures from the still-missing physical gauge carrier. Causal Memory Gravity V Supplement studies a corrected SU(2) frame constructed from the lowest graph modes. Across 20 independent causal-DPN realizations with 2,000 nodes, the measured spectral ratio is (3.925\pm0.014), establishing a reproducible benchmark within the tested graph family. The associated endpoint-factorized links have exactly trivial closed-loop holonomy, so they provide a frame-smoothness diagnostic rather than a physical gauge connection. The supplement defines the additional ingredients required for a conditional Maxwell continuum while keeping graph-spectral separation distinct from a transfer gap, correlation mass, or Yang–Mills mass gap. Causal Memory Gravity V Addendum updates the Maxwell plaquette, incidence, Hodge, and export analysis on the recovered DPN complex containing 2,000 nodes, 9,203 edges, and 4,326 faces. The discrete algebraic and Hodge checks remain valid, but the provenance audit shows that the faces are post-hoc graph cycles and the historical edge angles originate from endpoint-factorized frames. Their nonzero scalar curls are therefore structural diagnostics, not the holonomy of a derived photon connection. The addendum preserves the verified finite-complex results, provides an export protocol for a future physical carrier and action provider, and identifies the fine-structure calculation as an inverse calibration rather than a prediction. For the full CMG corpus, theory map, and related materials, visit: https://cmg.beogradpc.com/