Unraveling the hierarchical structure-property relationships is the central challenge of materials science, necessitating the interpretation of data across vast physical scales from micro to macro. Despite the rapid integration of Large Multimodal Models (LMMs) into scientific workflows, existing scientific benchmarks primarily focus on general chart interpretation or isolated common-sense reasoning, failing to capture reasoning ability across intricate physical dimensions. To address this, we introduce CSMBench, a dataset comprising 1,041 high-quality figures curated from premier journals up to September 2025. CSMBench categorizes data into four scientifically distinct regimes: atomic, micro, meso, and macro scales, strictly aligning with the focus and definitions in materials study. Through open-ended figure description and multiple-choice caption matching tasks, we evaluate state-of-the-art open-source and closed-source models. Our analysis identifies that performance varies significantly across physical scales due to the distinct visual characteristics, highlighting the limitations of current generalist models and identifying critical directions for achieving hierarchical and accurate understanding in materials research. The CSMBench is publicly released at: https://huggingface.co/datasets/lututu/CSMBench.

翻译：揭示多层次结构-性能关系是材料科学的核心挑战，这需要解读从微观到宏观跨越巨大物理尺度的数据。尽管大规模多模态模型（LMMs）已迅速融入科学研究流程，但现有科学基准测试主要聚焦于通用图表解读或孤立的常识推理，未能捕捉其在复杂物理维度上的推理能力。为此，我们提出CSMBench数据集，包含截至2025年9月从顶级期刊精选的1,041张高质量图像。CSMBench将数据严格依据材料研究的焦点与定义，划分为四个科学上截然不同的领域：原子尺度、微观尺度、介观尺度和宏观尺度。通过开放式图像描述与多选题式标题匹配任务，我们评估了当前最先进的开源与闭源模型。分析表明，由于不同物理尺度的视觉特征存在显著差异，模型性能在物理尺度间呈现显著变化，这揭示了当前通才模型的局限性，并为实现材料研究中多层次、准确的理解指明了关键方向。CSMBench已在以下链接公开发布：https://huggingface.co/datasets/lututu/CSMBench。