Detecting and interpreting polarized online content is increasingly crucial as online platforms become central to public information exchange. We present an efficient adaptation of large language models for multi-label polarization classification in SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Our single-forward-pass inference method outperforms baseline multi-step decoding approaches for multi-label classification by reducing error propagation while improving inference efficiency. Beyond performance and efficiency analysis, we investigate the cross-lingual transferability of the system, observing statistically significant generalization within language families, a result that offers a practical path for low-resource language adaptation. Our system ranked 1st in 8 languages for Subtask 1 and 6 languages for Subtask 2, and placed in the top 5 for 16 out of 22 languages across both subtasks.Overall, we provide a simple, effective, and efficient solution for multilingual polarization classification.