As Large Multimodal Models (LMMs) become more capable, there is growing interest in evaluating their reasoning processes alongside their final outputs. However, most existing benchmarks remain focused on English, overlooking languages with rich linguistic and cultural depth such as Arabic. To address this gap, we introduce the Comprehensive Arabic Multimodal Reasoning Benchmark (ARB), the first benchmark designed to evaluate step-by-step reasoning in Arabic across both textual and visual modalities. ARB covers 11 diverse domains and over 40 subfields, including visual reasoning, optical character recognition, scientific analysis, and cultural interpretation. It comprises 2,219 multimodal samples paired with over 8K human-curated reasoning steps and corresponding actions, verified through a human-in-the-loop process. We evaluated 15 state-of-the-art open- and closed-source LMMs and found persistent challenges in coherence, faithfulness, and cultural grounding. ARB provides a structured framework for diagnosing multimodal reasoning in underrepresented languages, marking a critical step toward inclusive, transparent, and culturally aware AI systems. The benchmark, rubric, and evaluation suite are publicly available

Arabic calligraphy represents one of the richest visual traditions of the Arabic language, blending linguistic meaning with artistic form. Although multimodal models have advanced across languages, their ability to process Arabic script, especially in artistic and stylized calligraphic forms, remains largely unexplored. To address this gap, we present DuwatBench, a benchmark of 1,272 curated samples containing about 1,475 unique words across 6 classical and modern calligraphic styles, each paired with sentence-level detection annotations. The dataset reflects real-world challenges in Arabic writing, such as complex stroke patterns, dense ligatures, and stylistic variations that often challenge standard text recognition systems.Using DuwatBench, we evaluated 13 leading Arabic and multilingual multimodal models and showed that while they perform well in clean text, they struggle with calligraphic variation, artistic distortions, and precise visual–text alignment. By publicly releasing DuwatBench and its annotations, we aim to advance culturally grounded multimodal research, foster fair inclusion of Arabic language and visual heritage in AI systems, and support continued progress in this area. Our dataset and code are publicly available.