Online polarization has become a central challenge in digital discourse, characterized by hostility, identity-based division, and culturally dependent expressions that vary across languages. Automatically detecting such phenomena is particularly difficult in multilingual settings, where semantic nuance and implicit rhetoric complicate cross-lingual generalization.In this context, we participate in POLAR, a shared task at SemEval 2026 on multilingual polarization detection and categorization across 22 languages. We compare three modeling paradigms: multilingual encoder fine-tuning, translation-based transfer learning, and prompting-based generative reasoning. For the multi-label categorization task, we introduce a two-stage cascaded architecture to mitigate false positives under severe class imbalance.Our results show that multilingual encoders achieve the most robust performance for binary detection, whereas reasoning-based prompting is competitive for fine-grained category classification. This comparative study highlights the strengths and limitations of each paradigm for cross-lingual polarization analysis.