Protocol Comparison of Tapered and Step Drives in a Quenched Spin Network

This repository contains the manuscript and reproducibility artifacts for "Protocol Comparison of Tapered and Step Drives in a Quenched Spin Network." Abstract We investigate the performance of two time-dependent drive protocols in a quenched, nine-node relational spin network: a linearly tapered controller, G_{10k}, and a binary step controller, M_{\text{Step}}. The study evaluates target acquisition and matched-success positive-drive work, W_+, across two benchmark conditions designed to test controller performance under baseline and increased localized coupling. Gate 2 reports a 31.6% reduction in mean W_+ for G_{10k} relative to M_{\text{Step}} on the N=89 concordant-success subset. In Gate 3, a heavy-motif condition multiplies the couplings on three adjacent edges by five. The reported acquisition rates are 96/100 for G_{10k} and 78/100 for M_{\text{Step}}, with 20 discordant cases favoring the linear taper and two favoring the step controller. On the N=76 concordant-success subset, mean W_+ is reduced by 8.25% under G_{10k}. These results do not support an a priori hypothesis, specified after a pilot on other seeds, that the step controller would overtake the linear taper under increased structural tension. The findings establish a benchmark-specific comparison of controller schedules, not a general thermodynamic optimality result. Interpretation is limited to the stated network, drive schedules, integration procedure, and outcome definitions; reproducibility depends on verification of the underlying data and analysis artifacts. Included Artifacts Protocol_Comparison_Quenched_Spin_Network.pdf: The full manuscript detailing the Hamiltonian dynamics, area-matched benchmark results, and a proposed structural correspondence to the Law of One and Reciprocal System (RS2) frameworks. task81_ablation.py: A reproducible NumPy tensor script executing the complete Seed 888 ablation, mapping the global energy minima, computing exact McNemar paired statistics, and tracking the post-cutoff trajectory divergence of the network.