大型语言模型（LLMs）正越来越多地被用作在战略和社会互动中自主的代理。尽管最近的研究表明，给LLMs分配人格特质可以影响其行为，但人格引导在受控条件下如何影响合作仍不明确。在本研究中，我们通过重复的囚徒困境游戏，研究了人格引导对LLM代理合作行为的影响。基于Big Five框架，我们首先使用Big Five Inventory测量三个模型的基本人格分数，即GPT-3.5-turbo、GPT-4o和GPT-5。然后我们比较了在基准条件和人格信息条件下行为的差异，并进一步分析了单独操纵每个个性维度到极端值的影响。我们的结果表明，宜人性是促进所有模型合作的主导因素，而其他人格特质影响有限。显式的人格信息会增加合作，但也可能提高被利用的脆弱性，尤其是在早期版本的模型中。相比之下，后期版本的模型表现出更具选择性的合作。这些发现表明，人格引导作为一种行为偏差，而非确定性控制机制。
英文摘要：
Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects cooperation under controlled conditions remains unclear. In this study, we examine the effects of personality steering on cooperative behavior in LLM agents using repeated Prisoner's Dilemma games. Based on the Big Five framework, we first measure basic personality scores of three models, GPT-3.5-turbo, GPT-4o, and GPT-5, using the Big Five Inventory. We then compare behavior under baseline and personality-informed conditions, and further analyze the effects of independently manipulating each personality dimension to extreme values. Our results show that agreeableness is the dominant factor promoting cooperation across all models, while other personality traits have limited impact. Explicit personality information increases cooperation but can also raise vulnerability to exploitation, particularly in earlier-generation models. In contrast, later-generation models exhibit more selective cooperation. These findings indicate that personality steering acts as a behavioral bias rather than a deterministic control mechanism.
Do Personality Traits Interfere? Geometric Limitations of Steering in Large Language Models
Network Analysis and Social Influence Modelling (NASIM) Lab（网络分析与社会影响建模实验室）；School of Physics Maths and Computing（物理数学计算学院）；The University of Western Australia（西澳大学）；School of Computing（计算学院）；Macquarie University（麦觉瑞大学）
A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China（东北大学计算机科学与工程学院，沈阳 110819，中国）；School of Computing and Information Systems, Singapore Management University, Singapore 178902, Singapore（新加坡管理大学计算机与信息系统学院，新加坡 178902，新加坡）；School of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, China（东北大学计算机与通信工程学院，秦皇岛 066004，中国）
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；TUM University Hospital（慕尼黑技术大学医院）；Google DeepMind（谷歌DeepMind）；Google Research（谷歌研究）；Imperial College London（伦敦帝国学院）
Department of Statistics and Operations Research, UNC-Chapel Hill（统计与运筹学系，北卡罗来纳大学 Chapel Hill 分校）；Imperial College Business School, Imperial College London（帝国理工学院伦敦校区商学院）
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Payoff scaling shapes cooperation in LLM agents across languages
Faculty of Information Technology, University of Science (HCMUS), Ho Chi Minh City, Vietnam（信息技术学院，科学大学（HCMUS），胡志明市，越南）；Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam（计算机科学与工程学院，胡志明市技术大学（HCMUT），胡志明市，越南）；Vietnam National University – Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam（越南国家大学——胡志明市（VNU-HCM），胡志明市，越南）；Luxembourg Institute of Science and Technology (LIST), Luxembourg（卢森堡科学与技术研究所（LIST），卢森堡）；School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom（计算、工程与数字技术学院，泰赛德大学，米德尔斯布罗，英国）
