心理增强型AI智能体
Psychologically Enhanced AI Agents
AI总结：
本研究提出MBTI-in-Thoughts框架，基于MBTI人格理论通过提示工程为LLM智能体注入人格，实现行为调控，验证了其在多任务中的效果及对其他心理学框架的泛化性，无需微调即可构建心理增强型AI智能体。
摘要：
我们提出MBTI-in-Thoughts框架，通过基于心理学的人格条件化提升大语言模型（LLM）智能体的效能。该方法以迈尔斯-布里格斯类型指标（MBTI）为基础，通过提示工程为智能体注入不同人格原型，实现沿人类心理学两大基础维度——认知与情感——的行为调控。研究表明，这种人格启动在多类任务中能产生一致且可解释的行为偏差：情感表达型智能体在叙事生成任务中表现更优，分析型启动的智能体在博弈论场景中会采用更稳定的策略。该框架支持开展结构化多智能体通信协议实验，且发现交互前的自我反思能提升合作水平与推理质量。为确保人格特质的持续性，我们引入官方16Personalities测试进行自动化验证。尽管研究聚焦于MBTI，但我们证明该方法可无缝推广至其他心理学框架，如Big Five（大五人格）、HEXACO（六因素人格模型）或Enneagram（九型人格）。通过搭建心理学理论与LLM行为设计的桥梁，我们无需微调即可为心理增强型AI智能体奠定基础。
英文摘要：
We introduce MBTI-in-Thoughts, a framework for enhancing the effectiveness of Large Language Model (LLM) agents through psychologically grounded personality conditioning. Drawing on the Myers-Briggs Type Indicator (MBTI), our method primes agents with distinct personality archetypes via prompt engineering, enabling control over behavior along two foundational axes of human psychology, cognition and affect. We show that such personality priming yields consistent, interpretable behavioral biases across diverse tasks: emotionally expressive agents excel in narrative generation, while analytically primed agents adopt more stable strategies in game-theoretic settings. Our framework supports experimenting with structured multi-agent communication protocols and reveals that self-reflection prior to interaction improves cooperation and reasoning quality. To ensure trait persistence, we integrate the official 16Personalities test for automated verification. While our focus is on MBTI, we show that our approach generalizes seamlessly to other psychological frameworks such as Big Five, HEXACO, or Enneagram. By bridging psychological theory and LLM behavior design, we establish a foundation for psychologically enhanced AI agents without any fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
