Abstract

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.

(http://www.autoadmit.com/thread.php?thread_id=5770675&forum_id=2Elisa#49242999)