Resolution Turnover in Synthetic Multiscale Dynamics: Matched-Information Tests of Coarse-to-Fine Constraint Feedback

This preprint presents a computational study of resolution turnover, a multiscale architecture developed within the Helical Actualization Theory (HAcT) framework. In the model, paired fine-scale streams interact, their relation is compressed into a coarse representation, and the resulting coarse information returns not as a reconstructed state but as a constraint on subsequent fine-scale dynamics. The study uses a sequence of synthetic model-selection experiments to prune alternative mechanisms before testing a frozen architecture against matched-information controls. In equal-information comparisons at resolutions N = 3, 6, and 9, turnover feedback produced lower joint error than feedforward use of the same coarse statistic. A 13-scenario robustness battery at each resolution—39 scenario-resolution comparisons in total—retained a positive mean turnover advantage with paired 95% intervals above zero in every cell. One strict heavy-tail stability criterion failed in a single extreme-input trajectory and is retained as an explicit limitation. The results support the computational viability of coarse-to-fine constraint return within the tested synthetic system. They do not establish HAcT as a universal physical, cosmological, or consciousness theory. This manuscript is a preprint and has not been peer reviewed.