EvolvingWorld: 一个用于交互式文学世界中角色扮演智能体与世界模型共同演化的开放架构框架

摘要

本文介绍了EvolvingWorld——一个面向交互式文学世界中角色与世界协同演化的框架与基准。现有系统要么将交互式文学模拟视为静态角色模仿，要么视为孤立的场景生成，未能捕捉角色与世界随时间共同演化的过程。为解决这一问题，EvolvingWorld将文学模拟建模为一个长时程过程：角色进行交互、场景逐步推进，且角色和世界状态持续更新。不同于依赖固定模式的先前系统，EvolvingWorld采用开放模式框架，支持跨多样文学世界的模拟。该框架由两个耦合模块组成：一个用于多角色扮演与持久档案演化的角色智能体，以及一个基于大语言模型的世界模型，负责全局及地点/实体层级的状态维护与场景推进。基于此架构，我们制定了7个可训练任务，涵盖场景初始化、交互生成与状态更新。我们从57本书籍中构建数据集，生成138,596个监督训练样本和222个测试快照。此外，我们引入了一种轨迹层级的大模型作为评判者的评估协议，涵盖10个维度和20个指标。实验表明，EvolvingWorld通过有效维护持久且连贯的角色与世界发展，能够改进长时程模拟。

English

This paper introduces EvolvingWorld, a framework and benchmark for character and world co-evolution in interactive literary worlds. Existing systems either treat interactive literary simulation as static persona imitation or isolated scene generation, failing to capture how characters and worlds evolve together over time. To address this, EvolvingWorld models literary simulation as a long-horizon process where characters interact, scenes progress, and character and world states are persistently updated. Unlike prior systems relying on fixed schemas, EvolvingWorld adopts an open-schema framework to support simulation across diverse literary worlds. The framework consists of two coupled modules: a Character Agent for multi-character role-play and persistent profile evolution, and an LLM-based World Model for global and location/entity-level state maintenance and scene progression. Based on this architecture, we formulate 7 trainable tasks for scene initialization, interaction generation, and state update. We construct a dataset from 57 books, producing 138,596 supervised training samples and 222 snapshots for testing. Furthermore, we introduce a trajectory-level LLM-as-Judge evaluation protocol spanning 10 dimensions and 20 metrics. Experiments show that EvolvingWorld can improve long-horizon simulation by effectively maintaining persistent, coherent character and world development.