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[2602.22157] Dynamic Personality Adaptation in Large Language Models via State Machines
Summary
This paper presents a model-agnostic framework for dynamic personality adaptation in Large Language Models (LLMs) using state machines, enhancing their interaction capabilities in complex dialogues.
Why It Matters
As LLMs are increasingly used in interactive contexts like education and customer support, their ability to adapt personality traits based on conversation dynamics is crucial. This research offers a novel approach to improve user engagement and effectiveness in such applications.
Key Takeaways
- Introduces a state machine framework for personality adaptation in LLMs.
- Demonstrates effective personality modulation based on user interactions.
- Maintains performance with lightweight classifiers, enhancing accessibility.
- Applicable in diverse fields such as education and customer support.
- Facilitates user behavior influence, aiding in training scenarios.
Computer Science > Computation and Language arXiv:2602.22157 (cs) [Submitted on 25 Feb 2026] Title:Dynamic Personality Adaptation in Large Language Models via State Machines Authors:Leon Pielage, Ole Hätscher, Mitja Back, Bernhard Marschall, Benjamin Risse View a PDF of the paper titled Dynamic Personality Adaptation in Large Language Models via State Machines, by Leon Pielage and 4 other authors View PDF HTML (experimental) Abstract:The inability of Large Language Models (LLMs) to modulate their personality expression in response to evolving dialogue dynamics hinders their performance in complex, interactive contexts. We propose a model-agnostic framework for dynamic personality simulation that employs state machines to represent latent personality states, where transition probabilities are dynamically adapted to the conversational context. Part of our architecture is a modular pipeline for continuous personality scoring that evaluates dialogues along latent axes while remaining agnostic to the specific personality models, their dimensions, transition mechanisms, or LLMs used. These scores function as dynamic state variables that systematically reconfigure the system prompt, steering behavioral alignment throughout the this http URL evaluate this framework by operationalizing the Interpersonal Circumplex (IPC) in a medical education setting. Results demonstrate that the system successfully adapts its personality state to user inputs, but also influences user behavior, the...
