Our two systems for StanceEval-2026, Arabic stance detection on Mawqif-XT, placed 1st of 31 on Track 1 (Favg2 = 0.8994) and 2nd of 22 on Track 2 (0.9357). Track 1 averages six zero shot readings of each tweet by frontier models from two providers, and calls a fine-tuned Arabic pipeline only to break exact ties. Track 2 is one reader, given a short generated description of the target so it can score stance expressed through a related topic. The leaderboard publishes three aggregate scores over the same predictions, and reading them shows that about 60% of our errors on both tracks are tweets annotated NONE that we placed in a stance class. Because the score ignores that class, the gain from moving such a row is fixed before the row is read. We derive the precision a repair must reach, and nine rankings plus an oracle from the corpus’s own annotation columns all fall short of it.