Causal Memory Gravity Seed

We demonstrate that graph-based telegraph dynamics exhibitstructural localization that statistically selects dynamicalregimes (underdamped vs. overdamped), and that this selection is operationally detectable from partial, noisy timeseries observations without spectral knowledge. Using 50 independent graph realizations (500 graphs total, n = 60-103nodes) and analyzing 18 nontrivial Laplacian eigenmodes pergraph, we establish three core findings via multi-seed validation: (C1) Certain topologies generate localized eigenmodes(272/500 graphs, 54% hit rate), (C2) localization exhibitspopulation-level coupling to regime selection (∆r = 0.211,CI95 = [0.175, 0.247], p < 0.001) with 38% seed-to-seedstability validating Causal Memory Gravity (CMG) predictions, and (C3) regimes are operationally detectable achieving PR-AUC = 0.853. The 50-fold sample increase (10 →500 graphs) yields 7× tighter confidence intervals and establishes statistical significance for the localization-regime coupling that was ambiguous in single-seed analysis. The 38%seed stability validates CMG’s prediction that “structure mayfluctuate while causal constraints persist,” distinguishing genuine structure-dependence from null effects (≈5% expected)or universal laws (≈90% expected).Keywords: telegraph dynamics · graph Laplacian · operational detection · structural localization · multi-seed validation· Causal Memory Gravity