ArA-DF 2026 is a shared task on binary Arabic speech deepfake detection, with two tracks: dialect generalisation and acoustic robustness. For both we submit rank-normalised score fusions of complementary detectors. Track 1 fuses five components in two stages: three fine-tuned XLS-R-300M networks and two ResNet-18 classifiers over linear-prediction residual and group-delay representations. Track 2 fuses two XLS-R-300M detectors differing in augmentation strategy. We reach 2.8961 EER on Track 1 and 2.7626 on Track 2, against baselines of 14.54 and 27.11. Our analysis on the released test labels shows individual EER to be a poor guide to fusion contribution, which depends on error independence: both fusions improve on their best member by over 40%, and components far weaker in isolation than any we submitted improve them further.