This paper describes our system for StanceEval 2026 Track 2, which requires predicting stance toward targets not seen during training. We explored fine-tuning different Arabic BERT encoder models, using class weighting techniques, incorporating auxiliary sentiment and sarcasm tasks in sequential and parallel multi-task setups, and focal and margin-based contrastive losses. We also tried zero-shot and few-shot prompting, self-consistency with escalation, LoRA fine-tuning, and an ensemble approach. Our best-performing submission on the official blind test set was a training-free few-shot prompting approach using Gemini 2.5 Flash with an extended prompt, achieving an F_avg2 of 87.41 and an F_avg3 of 76.94, and ranking 8th out of 13 teams. This result shows that a simpler, training-free prompting approach can generalize to unseen targets more effectively than fine-tuned and ensemble systems.