We describe our system for Track 1 (dialectgeneralization) of the ArA-DF-2026 Arabic speech deepfake detection shared task. Starting from the organizers’ XLS-R+AASIST baseline (14.65% EER on the development test set), we show that (i) raw-waveform augmentation (RawBoost) roughly halves the error rate, (ii) replacing the AASIST back-end with a simple mean-pooling head over a posttrained self-supervised frontend removes a hard performance ceiling that persists even after full convergence, and (iii) a large, architecturally diverse ensemble is the only lever that keeps improving once single-model performance saturates. Our final system, a 13-member weighted ensemble spanning three frontend sizes, two back-ends, and four training-data recipes, reaches 2.34% EER on the development-test leaderboard and 2.42% EER on the official held-out blind evaluation set (144,210 utterances, 9× larger than the development test). We also report two negative results of practical value: injecting a large (54k-clip) multi-system Arabic text-to-speech corpus and a vocoder-copy-synthesis pseudospoof corpus both degrade accuracy, indicating that indiscriminately adding synthetic speech is harmful when its generation artifacts do not match the target domain. Our code is publicly available at https://github.com/Body123/aradf-2026-saar-df.