DRAGON：一个用于基于证据的视觉推理的图示基准
DRAGON: A Benchmark for Evidence-Grounded Visual Reasoning over Diagrams
AI总结：
DRAGON基准评估模型在图示中基于证据的视觉推理能力，要求模型定位支持答案的视觉元素，包含图表、地图等，通过标注数据和评估框架检验八种最新视觉语言模型的定位能力。
摘要：
图示问答（DQA）要求模型解读结构化的视觉表示，如图表、地图、信息图、电路图和科学图示。最近的视觉语言模型（VLMs）在这些任务上常能获得高准确度，但正确答案并不保证模型在预测时基于支持预测的图示区域进行推理。模型可能依赖文本关联或数据集中的偶然因素，而未能识别出验证答案所需的视觉证据。这一限制阻碍了图示推理的可靠评估并降低了可解释性。我们引入DRAGON，一个用于评估图示中基于证据的视觉推理的基准。给定一个图示、一个问题和正确答案，模型必须预测对应的边界框，这些边界框对应于支持答案的视觉元素。这些证据区域可能包括承载答案的组件、文本标签、图例、坐标轴、连接器及其他参与推理过程的结构。DRAGON数据集包含从六个图示问答数据集中收集的11,664个标注问题实例：ChartQA、Circuit-VQA、InfographicsVQA、MapIQ、MapWise和AI2D。我们发布了一个包含2,445个实例的基准测试集，配有经人类验证的推理证据标注，并提供标准化的评估框架。我们评估了八种最近的VLMs，并分析了它们在不同图示领域中本地化推理证据的能力。DRAGON使图示推理的系统评估成为可能，并支持未来基于视觉证据进行预测的模型研究。
英文摘要：
Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics, circuit schematics, and scientific diagrams. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions that support the prediction. Models may instead rely on textual correlations or dataset artifacts without identifying the visual evidence required to verify the answer. This limitation prevents reliable evaluation of diagram reasoning and reduces interpretability. We introduce DRAGON, a benchmark for evaluating evidence-grounded visual reasoning in diagrams. Given a diagram, a question, and the correct answer, a model must predict bounding boxes that correspond to the visual elements required to justify the answer. These evidence regions may include answer-bearing components, textual labels, legends, axes, connectors, and other supporting structures involved in the reasoning process. The DRAGON dataset contains 11,664 annotated question instances from six diagram QA datasets: ChartQA, Circuit-VQA, InfographicsVQA, MapIQ, MapWise, and AI2D, with a 2,445-instance test set carrying human-verified evidence annotations and a standardized evaluation framework. Our evaluation on recent VLMs reveals that even Claude Opus 4.6 achieves a Grounding IoU of only 23.8% on AI2D, showing that faithful visual grounding remains an open challenge. DRAGON supports future research on models that ground their predictions in visual evidence.
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