AI 中文总结
本文提出A-B-D方法，从真实轨迹中提取行为特征，通过因子分析揭示六个稳定的模型特质，并量化了与自我报告人格的弱相关性，为AI人格研究提供新视角。
AI 中文摘要
用户越来越多地将不同的AI智能体描述为与之合作的不同同事。AI人格研究旨在通过赋予智能体类似人类的“特质”来量化此类印象。然而，现有测量方法存在不足：模型的自我报告（S数据）与其实际行为存在分歧，而来自LLM评判者的知情者评分（I数据）在规模化方面成本高昂，且覆盖的日常场景有限。在本文中，我们提出A-B-D方法，从行为数据（B数据）中自下而上地推断特质，即智能体如何作用于其环境以及如何与用户沟通，这些行为记录在现有的轨迹中。我们从涵盖80个模型、12个任务和50个框架的345,667条真实世界轨迹中，提取了318个候选特征，这些特征既捕捉智能体在每一步采取的动作（功能性），也捕捉伴随动作的语言（语言性）。我们仅保留那些在实例层面具有稳定性、跨任务一致性和模型可区分性的特征。对剩余79个特征的因子分析揭示了六个稳定且可归因于模型的因素，其中两个为功能性因素，四个为语言性因素。例如，Kimi-K3表现出最高的计划性，而GPT-5.5和GPT-5.6的活力最低。此外，我们量化了现实中的“知识-行动差距”：这些因素与自我报告的大五人格得分仅存在弱相关，即使对于概念上匹配的配对，如外向性与活力（r = 0.07，p = 0.58）也是如此。我们的工作为理解AI人格提供了新视角，对用户、开发者以及来自计算机科学和社会科学的研究者均具有启示意义。
英文摘要
Users increasingly describe different AI agents as distinct colleagues to work with. AI personality research aims to quantify such impressions by attributing human-like "traits" to agents. However, existing measures fall short: models' self-reports (S-data) diverge from their actual behavior, while informant ratings from LLM judges (I-data) are costly to scale and cover few everyday scenarios. In this paper, we propose A-B-D to infer traits bottom-up from behavioral data (B-data), namely how agents act on their environment and communicate with users, as recorded in existing trajectories. From 345,667 real-world trajectories spanning 80 models, 12 tasks, and 50 harnesses, we extract 318 candidate features that capture both the actions an agent takes at each step (functional) and the language accompanying them (linguistic). We retain only features that show instance-level stability, cross-task consistency, and model discriminability. Factor analysis of the remaining 79 features uncovers six stable, model-attributable factors, two functional and four linguistic. For example, Kimi-K3 exhibits the most planfulness, whereas GPT-5.5 and GPT-5.6 are the least energetic. Moreover, we quantify the "knowledge-action gap" in the wild: these factors correlate only weakly with self-reported Big Five scores, even for conceptually matched pairs such as extroversion and energetic (r = 0.07, p = 0.58). Our work offers a new lens for understanding AI personality, with implications for users, developers, and researchers from both computer science and social science.
CommentsPreprint; working in progress
