Predicting continuous Valence and Arousal scores across diverse languages poses significant challenges due to typological differences and the difficulty of modeling affective intensity. We introduce AdaptStance, a parameter-efficient framework designed for the SemEval-2026 Task 3 benchmark. To address cross-lingual disparities, AdaptStance routes inputs through resource-specific pipelines: direct regression with a hybrid concordance loss for high-resource languages, and an auxiliary multi-task mechanism to stabilize regression in low-resource and non-Western contexts. Architectural analysis reveals that decoupling task heads benefits morphologically related languages, whereas joint representations act as crucial regularizers for distant language families. Ultimately, this lightweight approach achieves competitive performance over generative baselines, demonstrating the efficacy of targeted architectural alignment while identifying Valence as the primary bottleneck in continuous affect prediction. Our code is available on GitHub.