本研究探讨虚拟角色提示（persona prompting）如何影响多模态大语言模型（multimodal LLM）在城市感知场景下所生成的语言。我们基于1,200个带虚拟角色条件的代理与两个无虚拟角色对照组所产生的59,808条标注，分析了不同虚拟角色下的图像描述（captions）、合理性说明（justifications）及感知标签（perception tags）。结果表明：不同虚拟角色在图像描述上呈现高度一致性；合理性说明则随社会经济属性与政治属性呈现系统性差异；感知标签未显示出统计显著的虚拟角色相关差异，但可观察到微弱的效应趋势。主题分析进一步揭示，不同虚拟角色在解释相同场景时侧重不同的评价性主题。

We study how persona prompting shapes language generated by multimodal large language models in an urban perception setting. Using 59,808 annotations from 1,200 persona-conditioned agents and two no-persona settings, we analyze captions, justifications, and perception tags across personas. Results indicate strong convergence in captions for different personas, whereas justifications display systematic variation associated with socioeconomic and political attributes, while perception tags show no statistically significant persona-related differences, though effect trends are observed. Topic analysis further reveals that personas emphasize different evaluative themes when interpreting the same scenes.