Year: 2025
Type: conference-paper
Abstract: Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems.Prior efforts focus on agent societies created from scratch, assigning agents with newly defined personas.However, simulating established fictional worlds and characters remain largely underexplored, despite its significant practical value.In this paper, we introduce BOOKWORLD, a comprehensive system for constructing and simulating book-based multi-agent societies.BOOK-WORLD's design covers comprehensive realworld intricacies, including diverse and dynamic characters, fictional worldviews, geographical constraints and changes, e.t.c.BOOK-WORLD enables diverse applications including story generation, interactive games and social simulation, offering novel ways to extend and explore beloved fictional works.Through extensive experiments, we demonstrate that BOOKWORLD generates creative, high-quality stories while maintaining fidelity to the source books, surpassing previous methods with a win rate of 75.36%.The code and demo of this paper can be found at the project page: https://bookworld2025.github.io/.
Authors: Yiting Ran, Xintao Wang, Tian Qiu, Jiaqing Liang, Yanghua Xiao, Deqing Yang
Institution: Fudan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: Fudan University, Major Research Plan
OpenAlex ID: W4412945315