Psychologically Enhanced AI Agents
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866914022582910976 |
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| author | Besta, Maciej Chandran, Shriram Gerstenberger, Robert Lindner, Mathis Chrapek, Marcin Martschat, Sebastian Hermann Ghandi, Taraneh Iff, Patrick Niewiadomski, Hubert Nyczyk, Piotr Müller, Jürgen Hoefler, Torsten |
| author_facet | Besta, Maciej Chandran, Shriram Gerstenberger, Robert Lindner, Mathis Chrapek, Marcin Martschat, Sebastian Hermann Ghandi, Taraneh Iff, Patrick Niewiadomski, Hubert Nyczyk, Piotr Müller, Jürgen Hoefler, Torsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Psychologically Enhanced AI Agents Besta, Maciej Chandran, Shriram Gerstenberger, Robert Lindner, Mathis Chrapek, Marcin Martschat, Sebastian Hermann Ghandi, Taraneh Iff, Patrick Niewiadomski, Hubert Nyczyk, Piotr Müller, Jürgen Hoefler, Torsten Artificial Intelligence Computation and Language Computers and Society Human-Computer Interaction Multiagent Systems 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. |
| title | Psychologically Enhanced AI Agents |
| topic | Artificial Intelligence Computation and Language Computers and Society Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2509.04343 |