AnaFig: A Human-Aligned Dataset for Scientific Figure Analysis

Scientific Figure Analysis (SFA) aims to derive analytical insights from figures while incorporating background instructions. Unlike conventional tasks such as figure captioning or description generation, which focus on extracting surface-level information from the sole visual modality, SFA requires an intelligent system to summarize key patterns, infer implications, and contextualize scientific findings from visual and textual inputs. It demands not only visual recognition but also the integration of scientific knowledge, multimodal understanding, and contextual reasoning. In this work, we introduce an SFA dataset, AnaFig, comprising 2,000 high-quality samples across 56 domains. All samples are evaluated by using human-aligned five-dimensional scoring criteria, resulting 10,000 human-annotated score labels. The AnaFig dataset facilitates the assessment of three critical capabilities of multimodal large language models (MLLMs): adherence to complex instructions, multimodal perception, and analytical summarization. By building a new benchmark with widely used MLLMs, this study contributes to scientific knowledge discovery and reasoning, fostering the alignment of MLLMs and human experts in scientific analysis.