Zixin Zhao
2025
Multi-Agent Based Character Simulation for Story Writing
Tian Yu
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Ken Shi
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Zixin Zhao
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Gerald Penn
Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025)
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.