We describe our submission to Subtask~1b of ImageEval 2026.
The task consists of selecting the single visually valid statement from three candidates paired with an image, and is in both English and Modern Standard Arabic (MSA).
Our system uses a pre-trained 7B vision--language model in a zero-shot setting, without task-specific training or fine-tuning.
We formulate the task as a constrained selection problem and choose the answer using next-token log-probabilities.
To reduce answer-position bias, we apply a Latin-square rotation to the candidate statements.
Our system achieves CI scores of 0.0510 on the English test set and 0.0960 on the MSA test set,
reducing CI relative to the official 3B baselines reported on devtest by 83.7% and 80.4%, respectively.