Multi-Agent Based Character Simulation for Story Writing
Published in Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025), 2025
Recommended citation: Tian Yu, Ken Shi, Zixin Zhao, and Gerald Penn. 2025. Multi-Agent Based Character Simulation for Story Writing. In Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025), pages 87–108, Albuquerque, New Mexico, US. Association for Computational Linguistics. https://aclanthology.org/2025.in2writing-1.9/
This work proposes a novel multi-agent story-generation system that writes stories from a narrative plan. Traditional approaches tend to generate a section of text directly from its outline. Our system, by contrast, divides this elaboration process into role-play and rewrite steps, where the former step enacts the story in chronological order with LLM-backed character agents, and the latter step refines the role-play result to align with a narrative plan. We show that the stories produced by our system are preferable to two other LLM-based story-generation approaches. We attribute this advancement to the benefits of incorporating a character-based simulation strategy.
Recommended citation: Tian Yu, Ken Shi, Zixin Zhao, and Gerald Penn. 2025. Multi-Agent Based Character Simulation for Story Writing. In Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025), pages 87–108, Albuquerque, New Mexico, US. Association for Computational Linguistics.