We present our systems for ArGuard 2026 Track A, which aims to detect harmful content in Arabic memes. The track includes two tasks: a binary task for identifying hateful memes (A1) and a multi-label task for predicting specific categories (A2). For A1, our approach combines a fine-tuned Qwen3-VL sequence classification model, Arabic text encoders, and a Qwen text LoRA classifier. We then apply top-k calibration and make targeted adjustments based on model disagreements. On the official test set, our system reached a macro-F1 score of 0.7896 and an accuracy of 0.8200. For A2, we found that using thresholds from the development data caused us to overpredict common labels and miss rare ones. Our best A2 submission uses an ensemble of six Arabic text models, with label-frequency-aware top-k calibration and rare-label boosting. This approach achieved a macro-F1 of 0.3601 and a micro-F1 of 0.5150. Overall, we found that careful calibration of a few fine-tuned Arabic and multimodal classifiers contributed more to our results than using larger zero-shot vision-language models.