Detecting polarization in online discourse is important for understanding social fragmentation , yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing. In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2). We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs. For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting. For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves a macro F1 = 0.62. Prompt-language effects are model-and task-dependent, with some settings doing better with English prompts and others with Arabic prompts. Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL.

We present the Linguistic Security Institute (LSI) submission to the BAREC Shared Task 2026 (Task 1): predicting Arabic sentence readability on a 19-level ordinal scale. Our approach uses a logit-level ensemble of three Arabic pre-trained language models, with CAMeLBERT trained using an ordinal Earth Mover’s Distance loss and augmented by six SAMER Lexicon difficulty features. Controlled comparisons show that surface heuristics improve undertrained baselines but degrade converged models, and retrieval-augmented in-context learning yields no gains in this fine-grained setting. Our final ensemble achieves 81.7 QWK, ranking 1st among Constrained-track system submissions and 3rd among Open-track system submissions.