用大模型构建可交互的持久化游戏世界，让剧情与系统自动协同。
Orchestrated Reality: From Role-Play to Living, Playable Game Worlds -- LLM-Driven World Simulation as a Parameterized-Action POMDP
- 设计统一的世界状态树，由单一代理管理叙事与规则。
- 通过计划-差异-验证-应用流程确保状态变更合法且可追溯。
- 适合开发开放世界游戏或研究自主代理系统的研究人员。
许多游戏依赖叙事与系统追踪等级、非玩家角色行为及后果模拟；在沙盒与开放世界游戏中，将精心编写的故事与深度模拟世界结合一直成本高昂。基于大模型的世界提供了新路径：单一系统可协调数值状态、叙事风格、节奏与规则逻辑。实现这一目标需大模型维持持久的世界状态（谁在何处、刚发生了什么、当前何事为真），而现有部署系统无法做到：叙事以自由散文声明状态，缺乏有效表示，因此完全自主的游戏引擎仍不可行。本文将其视为架构选择而非语言模型局限，提出名为‘协奏现实’的框架，将世界作为由单例协调代理（类比桌游主持人）持有的核心对象。我们将人类玩家参与的大模型驱动游戏世界形式化为参数化动作部分可观测马尔可夫决策过程（POMDP）：状态为标准化的JSON实体树，动作分解为 (k, x_k)（离散意图类型加结构化参数），代理仅观测状态的叙事投影 o=O(s)，转移核 F 由大模型驱动的计划-差异-验证-应用（PDVA）流水线实现，提交经模式验证与内容哈希的JSON增量。本文给出形式化模型、一个JSON状态示例、单轮运行实例，并展示来自真实部署的15个典型事件案例。未来工作包括人类玩家实验验证、多非玩家角色并发智能及作为强化学习环境的部署。
原文摘要 · Abstract (English)
Many games rely on storytelling combined with systems that track levelling, NPC behaviour, and consequence simulation; bridging tightly-authored narrative with deeply-simulated worlds -- most acute in sandbox and open-world settings -- has been prohibitively expensive. LLM-driven worlds open a new path: a single harness can coordinate numerical state, narrative voice, storytelling pacing, and rule logic together. Realising this requires the LLM system to sustain a persistent world (who is where, what has just happened, what is currently true), which today's deployed systems do not: the narrative voice asserts state in free prose without any validated representation, so a fully autonomous game engine remains infeasible. We treat this as an architectural choice, not a limitation of language models, and report work in progress on a framework -- orchestrated reality -- that makes the world a canonical object owned by a singleton orchestration agent analogous to the tabletop-RPG Game Master (GM). We formalise an LLM-driven game world for a human player as a Parameterized-Action POMDP: state is a tree of canonical JSON entities, actions decompose as $a=(k, x_k)$ (a discrete intent kind plus structured JSON parameters), the agent observes only a narrative projection $o=O(s)$ of state, and the transition kernel $F$ is an LLM-driven Plan-Diff-Validate-Apply (PDVA) pipeline that commits schema-validated, content-hashed JSON deltas. We give the formal model, a JSON-state example, a worked single-turn example, and a catalogue of 15 illustrative incidents drawn from a real deployment showing the framework in action. Empirical validation through a planned human player study -- together with multi-NPC concurrent agency and deployment as an RL environment -- is situated as future work.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。