Scientific reasoning is a key aspect of human intelligence, requiring the integration of multimodal inputs, domain expertise, and multi-step inference across various subjects. Existing benchmarks for multimodal large language models (MLLMs) often fail to capture the complexity and traceability of reasoning processes necessary for rigorous evaluation. To fill this gap, we introduce SciVQR, a multimodal benchmark covering 54 subfields in mathematics, physics, chemistry, geography, astronomy, and biology. SciVQR includes domain-specific visuals, such as equations, charts, and diagrams, and challenges models to combine visual comprehension with reasoning. The tasks range from basic factual recall to complex, multi-step inferences, with 46% including expert-authored solutions. SciVQR not only evaluates final answers but also examines the reasoning process, providing insights into how models reach their conclusions. Our evaluation of leading MLLMs, including both proprietary and open-source models, reveals significant limitations in handling complex multimodal reasoning tasks, underscoring the need for improved multi-step reasoning and better integration of interdisciplinary knowledge in advancing MLLMs toward true scientific intelligence. The dataset and evaluation code are publicly available at https://github.com/CASIA-IVA-Lab/SciVQR.

翻译：科学推理是人类智能的核心要素，要求整合多模态输入、领域专业知识以及跨学科的多步推理能力。现有面向多模态大语言模型（MLLMs）的基准通常难以捕捉严格评估所需的推理过程的复杂性与可追溯性。为填补这一空白，我们提出SciVQR——一个涵盖数学、物理学、化学、地理学、天文学及生物学等54个子领域的多模态基准。SciVQR包含领域特定视觉元素（如公式、图表和示意图），并挑战模型将视觉理解与推理能力相结合的能力。任务涵盖从基础事实回忆到复杂多步推理的多个层次，其中46%的任务附有专家撰写的解答。SciVQR不仅评估最终答案，更深入分析推理过程，揭示模型得出结论的内在机制。我们对主流MLLMs（包括商业模型与开源模型）的评估表明，这些模型在处理复杂多模态推理任务时存在显著局限性，这凸显了在推动MLLMs迈向真正科学智能的过程中，亟需强化多步推理能力与跨学科知识的整合。数据集与评估代码已开源发布于https://github.com/CASIA-IVA-Lab/SciVQR。