EvolvingWorld：一個面向互動文學世界中角色扮演智能體與世界模型共同進化的開放模式框架

摘要

本文介紹了EvolvingWorld，一個用於互動式文學世界中角色與世界共同演化的框架與基準測試。現有系統要麼將互動式文學模擬視為靜態角色模仿，要麼視為孤立的場景生成，無法捕捉角色與世界隨著時間共同演化的過程。為解決此問題，EvolvingWorld將文學模擬建模為一個長期過程，在此過程中角色互動、場景推進，且角色與世界狀態持續更新。與依賴固定架構的既有系統不同，EvolvingWorld採用開放架構框架，以支援跨多樣文學世界的模擬。該框架由兩個耦合模組組成：一個用於多角色扮演與持續角色檔案演化的角色代理，以及一個基於大型語言模型的世界模型，負責全域及地點/實體層級的狀態維護與場景推進。基於此架構，我們制定了7項可訓練任務，涵蓋場景初始化、互動生成與狀態更新。我們從57本書籍中構建資料集，產生了138,596個監督式訓練樣本與222個測試快照。此外，我們引入了一個軌跡層級的以大型語言模型為評審的評估協議，涵蓋10個維度與20項指標。實驗結果顯示，EvolvingWorld能有效維持持續且一致的發展，從而改進長期模擬的表現。

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.