Multi-Agent Based Character Simulation for Story Writing (2025)

Abstract: 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.

## introduction recent advancements in large language models (llms) have significantly improved text coherence and fluency. researchers are now implementing automatic story generation and human-ai writing tasks using llms @xcite . traditionally, story generation involved a planning stage to sequence events, followed by a generation stage to elaborate these events into scenes @xcite . approaches like ipocl @xcite treat narrative planning as a search problem using character or author goals as guides @xcite . recent work proposes using llms as planning engines, such as agents' room @xcite , which uses multi-agent collaboration for narrative planning, and dramatron @xcite , which modularizes the generation process in a manner similar to screenplay writing. research shows that writers prefer modularizing story generation into smaller components, as it allows control over which parts of the process are automated @xcite @xcite . ## methodology this section describes our approach to the outlinebased creative writing task. we first provide an overview of our breakdown of the problem and then describe the individual components. ## conclusion in conclusion, our work is the first to integrate the concepts of fabula and syuzhet into a unified process for generating stories from a narrative plan. we decomposed story creation into two distinct phases/steps: a fabula generation phase (role-play step) driven by realistic, llm-backed character agents under the guidance of a director agent. this achieves a natural balance between authorial intent and character-driven conversation history. this is followed by a syuzhet modification phase (rewrite step), which refers to the conversation history and can potentially reuse the majority of dialogues and actions, only needing to manipulate their order, thus significantly reducing the difficulty of the actual story-content-writing process. our current approach assumes that a scene consists of a sequence of events occurring within a specific location and time frame. however, there are cases where scenes interleave between present conversations and flashbacks -for example when a character recalls a memory. as such, our sorting algorithm may fail to produce a strictly chronologically ordered plan for the role-play step. future work could address this limitation by sorting all events in the story with finer granularity, regardless of scene boundaries, to handle complex temporal structures better. additionally, while our system updates agents' memories to reflect only the information they should see, it does not explicitly enforce privacy during the role-play process. future work can improve upon this by implementing explicit privacy controls. lastly, our llm evaluation method aligns with human-tested criteria. since our project's goal is to assess the potential of agent-based simulation for story creation, future work can explore effective approaches for integrating human participants into this process.