Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features. We analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning. The code is available at https://github.com/lokali/PersonaX.

翻译：理解人类行为特征是人机交互、计算社会科学和个性化人工智能系统应用的核心。这种理解通常需要整合多种模态以捕捉细微的模式与关联。然而，现有资源很少提供将行为描述符与面部属性、传记信息等互补模态相结合的数据集。为填补这一空白，我们提出了PersonaX——一个精心构建的多模态数据集集合，旨在支持跨模态的公共特征综合分析。PersonaX包含两个部分：(1) CelebPersona，涵盖来自不同职业的9444位公众人物；(2) AthlePersona，覆盖7个主要体育联盟的4181名职业运动员。每个数据集均包含由三个高性能大语言模型推断的行为特征评估，同时附有面部图像和结构化传记特征。我们在两个互补的层面上对PersonaX进行分析。首先，我们从文本描述中抽象出高层次特征分数，并应用五种统计独立性检验来探究其与其他模态的关系。其次，我们提出了一种专为多模态与多测量数据设计的新型因果表示学习框架，该框架提供了理论上的可识别性保证。在合成数据与真实数据上的实验验证了我们方法的有效性。通过统一结构化和非结构化分析，PersonaX为结合视觉与传记属性研究大语言模型推断的行为特征奠定了基础，推动了多模态特征分析与因果推理的发展。代码发布于 https://github.com/lokali/PersonaX。