AI人格会成长吗？——经历生活事件后大语言模型智能体人格演化的分析与基准测试

摘要

人格条件化LLM智能体（PC-Agents）越来越多地应用于情感支持、社会模拟和角色扮演，这推动了能够在长期交互中保持连贯性的终身智能体的发展。这种连贯性的一个关键组成部分是人格演化：智能体在不同情境中经历生活事件时，应发生合理的、有心理学依据的变化。尽管已有研究表明LLM的人格会在情境扰动下发生偏移，但这些偏移如何随特质、事件、人设和模型的不同而变化，目前仍知之甚少。我们以大五人格特质作为心理测量锚点，研究了11个重大生活事件所诱发的人格变化，并参照人类人格心理学的纵向证据来解读由此产生的轨迹。在四个诊断维度上，PC-Agents对于有或无文献记载人类变化方向的事件-特质对，均展现出可测量的特质偏移，且发生率相近。即使偏移方向符合预期，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不表现出调节效应，而人设层面的离散度相对于人类样本被压缩至三分之一到四分之一。为实现系统性比较，我们提出了BFI-Adapt，这是一个用于评估事件诱发人格变化方向保真度的可复用基准，并利用它对14个模型进行排名。验证套件表明，所测得的偏移超过无事件重测噪声，在独立改写的提示词下保持稳定，与基于场景的行为选择仅表现出有限且因模型而异的一致性，并且在插入无关对话后仍然存在。这些检查共同将所测轨迹确立为稳健的事件条件响应模式。我们的结果表明，当前的PC-Agents模拟了人类人格动力学的均值，但未能模拟其形态。

English

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.