# Structured Personality Control and Adaptation for LLM Agents

arXiv: https://arxiv.org/abs/2601.10025 · HTML: https://mlsanity.com/paper/2601.10025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 19 of 19)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://mlsanity.com/paper/2310.17976/) (2023)
- [Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https://mlsanity.com/paper/2307.11760/) (2023)
- [Simulating Human-like Daily Activities with Desire-driven Autonomy](https://mlsanity.com/paper/2412.06435/) (2024)
- [Editing Personality for Large Language Models](https://mlsanity.com/paper/2310.02168/) (2023)
- [The Better Angels of Machine Personality: How Personality Relates to LLM Safety](https://mlsanity.com/paper/2407.12344/) (2024)
- [Human Simulacra: Benchmarking the Personification of Large Language Models](https://mlsanity.com/paper/2402.18180/) (2024)
- [Evolving Agents: Interactive Simulation of Dynamic and Diverse Human Personalities](https://mlsanity.com/paper/2404.02718/) (2024)
- [Personality-Driven Decision-Making in LLM-Based Autonomous Agents](https://mlsanity.com/paper/2504.00727/) (2025)
- [Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities](https://mlsanity.com/paper/2411.03252/) (2024)
- [Machine Mindset: An MBTI Exploration of Large Language Models](https://mlsanity.com/paper/2312.12999/) (2023)
- [Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation](https://mlsanity.com/paper/2404.02589/) (2024)
- [Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits](https://mlsanity.com/paper/2412.12510/) (2024)
- [PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurements](https://mlsanity.com/paper/2408.03337/) (2024)
- [Dynamic Generation of Personalities with Large Language Models](https://mlsanity.com/paper/2404.07084/) (2024)
- [Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics](https://mlsanity.com/paper/2503.17085/) (2025)
- [How Jungian Cognitive Functions Explain MBTI Type Prevalence in Computer Industry Careers](https://mlsanity.com/paper/2504.17248/) (2025)
- [Extroversion or Introversion? Controlling The Personality of Your Large Language Models](https://mlsanity.com/paper/2406.04583/) (2024)
- [Exploring Personality-Aware Interactions in Salesperson Dialogue Agents](https://mlsanity.com/paper/2504.18058/) (2025)

## Built on by (most cited first, 1 of 1)

- [Emergi-PersonaOS: A Persona Agent Operating System for Situational Adaptation and Controllable Evolution](https://mlsanity.com/paper/2609.27417/) (2026)

## Abstract (arXiv)

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.