AI 角色终于会「长大」了
以前的 AI 角色扮演像一张定格照片：角色设定固定，世界背景不变，故事推进只是机械地接话。这篇让角色和世界一起「活」起来——角色会随着互动改变性格和记忆，世界也会因事件而演化。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档案更新，另一个模块管理世界状态和场景推进。在长线模拟中，角色和世界的连贯性显著提升。它不是你明天能用上的，但如果你玩过那种「角色永远记不住你」的互动小说，这就是未来。
📄 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.