AI agents are increasingly used as low-cost proxies for early visualization evaluation. In an initial study of deliberately flawed charts, we test whether agents spontaneously penalise chart junk and misleading encodings without being prompted to look for errors. Using established scales (BeauVis and PREVis), the agent evaluated visualizations containing decorative clutter, manipulated axes, and distorted proportional cues. The ratings of aesthetic appeal and perceived readability often remained relatively high even when graphical integrity was compromised. These results suggest that un-nudged AI agent evaluation may underweight integrity-related defects unless such checks are explicitly elicited.

翻译：AI代理正日益被用作早期可视化评估的低成本替代方案。在一项针对刻意设计缺陷图表的初步研究中，我们测试了代理是否会在未被提示寻找错误的情况下，自发地对图表垃圾和误导性编码进行扣分。使用既定量表（BeauVis和PREVis），该代理评估了包含装饰性杂乱、操纵坐标轴和扭曲比例线索的可视化图表。即使图形完整性受损，美学吸引力和感知可读性的评分通常仍保持相对较高。这些结果表明，除非明确要求进行此类检查，否则未经引导的AI代理评估可能会低估与完整性相关的缺陷。