AI 角色终于会「长大」了：文学世界模拟新框架
以前的 AI 角色扮演只是「静态模仿」——角色永远一个样，世界也不会变。这篇论文让角色和世界一起「进化」：角色会记住经历、改变性格，世界也会随剧情推进更新状态。研究者从 57 本书里提取数据，训练了一个双模块系统：一个模块负责角色扮演和记忆更新，另一个模块负责管理世界状态和场景推进。测试表明，这种设计能让长故事里的角色和世界保持连贯发展。它不是你明天能用上的，但如果你玩过角色扮演游戏或看过互动小说，这就是让 AI 角色真正「活起来」的方向。
📄 原文摘要(英文)
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.