Structured Personality Control and Adaptation for LLM Agents

• URL: http://arxiv.org/abs/2601.10025v1
• Date: Thu, 15 Jan 2026 03:15:24 GMT
• Title: Structured Personality Control and Adaptation for LLM Agents
• Authors: Jinpeng Wang, Xinyu Jia, Wei Wei Heng, Yuquan Li, Binbin Shi, Qianlei Chen, Guannan Chen, Junxia Zhang, Yuyu Yin,
• Abstract summary: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI)<n>We present a framework that models LLM personality via Jungian psychological types.<n>This design allows the agent to maintain nuanced traits while dynamically adjusting to interaction demands.
• Score: 11.050618253938126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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