We describe Raid AI’s entry to Track 1 (Dialect Generalization) of the ArA-DF 2026 shared task on Arabic speech deepfake detection, which placed 1st of 15 teams at 0.8719% Equal Error Rate against a 14.54% official baseline. The configuration of the submitted system is proprietary and is not disclosed. We instead report the ablation behind it, re-scored on the official 144,210-clip evaluation partition against the labels released after the challenge, with bootstrap confidence intervals and paired tests throughout. On development-test, corpus and augmentation alone move a fixed architecture from 13.13% to 1.37% EER. Encoder scale is not monotonic: a 1B encoder beats a 2B under a recipe matched on corpus, augmentation, learning rate, class weights and effective batch size, though the gap is inside the run-to-run spread we measure from three seeds. Three-crop test-time augmentation improves all ten models tested, by 17.4% relative on average, and is the only effect immune to seed variance because it is measured within a checkpoint; a per-crop decomposition shows the gain comes from averaging rather than from choosing a better window.