Persona agent system adapts and evolves through long term interaction
Emergi-PersonaOS: A Persona Agent Operating System for Situational Adaptation and Controllable Evolution
Artificial Intelligence
Summary
People and digital helpers can build lasting relationships by having personalities that grow and change over time. The authors created Emergi-PersonaOS, a system that manages these personalities like a software operating system. It keeps track of traits, behaviors, and stories that shape the personality, and changes them based on interactions and memories. This allows digital agents to respond differently in situations while also learning and evolving in controlled ways over long interactions.
What this means in practice
- •For chatbot developers: Create chatbots that adapt their personality in real time and evolve based on long-term user interactions for more natural communication.
- •For video game designers: Develop non-player characters that maintain consistent personality traits while changing responses dynamically throughout gameplay narratives.
Authors
Haoluan Fu, Keni Chen, Xinyu Jia, Jinpeng Wang, Yuyu Yin
Abstract
Symbiosis between humans and digital beings offers a vision for the future of human--machine interaction. In enduring human--machine relationships, personality provides a foundation for continuity of identity, individuality in interaction, and development through experience. We investigate this capacity through persona agents as computational implementations and introduce Emergi-PersonaOS, a psychology-grounded operating system for managing persona objects throughout their lifecycle. The system organizes dispositional traits, characteristic adaptations, and narrative identity into a three-layer persona representation, distinguishing relatively enduring persona beliefs from their activation in the current persona state. During situational adaptation, it integrates the current interlocutor, relationship, event, and retrieved memories to infer a persona state and generate actions and replies; during long-term development, it records experiences and outcomes, and develops and evaluates revision candidates through change attribution, meaning-making, and behavioral testing. Belief updates are managed through explicit review, traceable evidence and version records, and the ability to reject candidates, making persona evolution controllable. Using television-character dialogue as longitudinal material, we demonstrate long-horizon system operation and examine its principal mechanisms in a concrete implementation. This work provides a computational framework for persona agents to maintain individual continuity, produce situation-specific expression, and develop through experience over sustained interaction.