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.

This paper presents the Gradient Descenders system for DialectSentEval Subtask 1 on multi-dialect Arabic sentiment analysis. Because general-purpose models often miss dialectal nuances, we compare three pretrained encoders with distinct linguistic orientations: MARBERTv2, DarijaBERT, and AraBERT. We formulate the task as three-class classification, employing a parameter-efficient head-only adaptation strategy that freezes encoder parameters. Our results show MARBERTv2 outperforms the others, achieving a Macro F1 of 80.11% and Accuracy of 81.90%, securing fifth place overall. These findings emphasize that backbone selection is critical; specifically, broad dialect-oriented pretraining yields the most transferable representations for multi-dialect generalization.