Fine-grained Arabic readability assessment aims to estimate how difficult an Arabic sentence is for its intended readers by assigning it to one of 19 ordered readability levels. This task is particularly challenging because of Arabic’s morphological complexity and the imbalanced distribution of examples across the levels. To address these challenges, we develop a system for the Sentence-Level Strict Track of the BAREC Shared Task 2026. Using only the official BAREC data, the system combines D3Tok-based morphological information, normalized surface text, and complementary features within a multi-seed ensemble of AraBERTv2-based regression models. Our submission achieved a QWK of 84.1% on the official Blind Test. This result indicates that the proposed combination provides competitive performance for fine-grained Arabic readability assessment.