用荣格心理类型构建可自适应的智能体人格，让大模型更像真人互动。
Structured Personality Control and Adaptation for LLM Agents
- 基于荣格心理学设计核心人格与临时适应机制
- 在多种挑战场景下实现连贯且情境敏感的交互表现
- 适合需要自然化人机交互的虚拟助手、社交模拟应用
大型语言模型正日益影响人机交互，从个性化助手到社会模拟。除了语言能力，研究者开始探索模型是否能表现出影响参与度、决策和真实感的人类特质。人格尤为关键，但现有方法难以兼顾细腻表达与动态适应。本文提出一个框架，通过荣格心理类型建模语言模型人格，整合三种机制：主导-辅助协调机制以保持核心人格一致性，强化-补偿机制实现情境临时适应，反思机制驱动长期人格演化。该设计使智能体在维持细腻人格特征的同时，能动态响应交互需求并逐步更新内在结构。通过迈尔斯-布里格斯性格类型指标问卷评估人格对齐性，并在多样化挑战场景中测试，结果表明具备演化的、人格感知的模型能支持连贯、情境敏感的交互，推动人机交互中的自然化智能体设计。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechanisms: a dominant-auxiliary coordination mechanism for coherent core expression, a reinforcement-compensation mechanism for temporary adaptation to context, and a reflection mechanism that drives long-term personality evolution. This design allows the agent to maintain nuanced traits while dynamically adjusting to interaction demands and gradually updating its underlying structure. Personality alignment is evaluated using Myers-Briggs Type Indicator questionnaires and tested under diverse challenge scenarios as a preliminary structured assessment. Findings suggest that evolving, personality-aware LLMs can support coherent, context-sensitive interactions, enabling naturalistic agent design in HCI.
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