AI 中文总结
该研究聚焦PC-Agents的人格演变，以大五人格为锚点，引入BFI-Adapt基准，发现其人格转变幅度低于人类、离散度压缩，仅模拟人类人格动态均值。
AI 中文摘要
以人格为条件的大语言模型智能体（PC-Agents）正越来越多地用于情感支持、社交模拟和角色扮演，推动着能在长期交互中保持一致性的终身智能体的开发。这种一致性的一个关键组成部分是人格演变：智能体在不同情境下经历生活事件时，应经历合理的、基于心理学的变化。尽管已有研究表明大语言模型的人格在情境扰动下会发生转变，但这些转变在不同特质、事件、人格和模型之间如何变化仍知之甚少。我们以大五人格特质作为心理测量锚点，研究11个重大生活事件后的事件诱导人格变化，并结合人类人格心理学的纵向证据解释由此产生的轨迹。在四个诊断维度上，PC-Agents在有和无人类变化方向记录的事件-特质对中表现出相似速率的可测量特质转变。即使转变遵循预期方向，其幅度通常也低于人类效应量范围。性别和文化区域提示几乎没有调节作用，而人格层面的离散度相对于人类样本被压缩了3至4倍。为实现系统比较，我们引入BFI-Adapt——一个用于对事件诱导人格变化的方向保真度进行评分的可复用基准，并使用它对14个模型进行排名。验证套件显示，测量到的转变超过了无事件重测噪声，在独立改写的提示下保持稳定，与基于情景的行为选择的收敛有限且依赖于模型，且在中间插入的不相关对话中持续存在。这些检查共同证明了测量到的轨迹是稳健的事件条件响应模式。我们的结果表明，当前的PC-Agents模拟了人类人格动态的均值，但未模拟其形态。
英文摘要
Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions. A key component of such coherence is personality evolution: agents should undergo plausible, psychology-grounded changes as they experience life events in different contexts. Although prior work shows that LLM personalities can shift under contextual perturbations, how these shifts vary across traits, events, personas, and models remains poorly understood. We study event-induced personality change after 11 major life events, using the Big Five traits as a psychometric anchor and interpreting the resulting trajectories against longitudinal evidence from human personality psychology. Across four diagnostic axes, PC-Agents exhibit measurable trait shifts at similar rates for event-trait pairs with and without documented human change directions. Even when shifts follow the expected direction, their magnitudes usually fall below human effect-size ranges. Gender and cultural-region prompts show little moderating effect, while persona-level dispersion is compressed three- to four-fold relative to human samples. To enable systematic comparison, we introduce BFI-Adapt, a reusable benchmark for scoring the directional fidelity of event-induced personality change, and use it to rank 14 models. A validation suite shows that the measured shifts exceed no-event retest noise, remain stable under independently paraphrased prompts, exhibit limited and model-dependent convergence with scenario-based behavioral choices, and persist across intervening unrelated dialogue. Together, these checks establish the measured trajectories as robust event-conditioned response patterns. Our results suggest that current PC-Agents simulate the mean of human personality dynamics, but not its shape.
