dorsal/arxiv
View SchemaStructured 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.10025vv1 |
| URL | https://arxiv.org/abs/2601.10025 |
| 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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"date_created": "2026-02-17T05:53:23.791000Z",
"date_modified": "2026-02-17T05:53:23.791000Z",
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"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.",
"arxiv_id": "2601.10025",
"authors": [
"Jinpeng Wang",
"Xinyu Jia",
"Wei Wei Heng",
"Yuquan Li",
"Binbin Shi",
"Qianlei Chen",
"Guannan Chen",
"Junxia Zhang",
"Yuyu Yin"
],
"categories": [
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Structured Personality Control and Adaptation for LLM Agents",
"url": "https://arxiv.org/abs/2601.10025",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "5adb9d13-b480-4ef4-a27f-ddc50e61675d",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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