When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations

ArXi:2603.22368v1 Announce Type: cross Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models (VLMs) perform well on many chart understanding tasks, their ability to detect misleading visualizations, especially when deception arises from subtle reasoning errors in captions, remains poorly understood.