We present Tarjih, our proposed system for the Stance Detection in Arabic Language shared task (StanceEval-2026). Rather than predicting stance directly, Tarjih formulates the task as verification among competing arguments. An instruction-tuned LLM generates one steelman argument for each stance label, and a second blind pass evaluates each argument against the tweet without access to the gold label. Each argument–tweet is then jointly encoded with AraBERT to model their compatibility. From this pipeline, we derive three systems: a gated classifier, a pair scorer, and a ranking read-out. The pair scorer achieves the best seen-target test Favg2 of 0.73, while the gated classifier performs best on the unseen-target track with 0.77. Overall, the lower test performance and ranking among the submitted systems indicate limited generalization across targets.