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.

翻译：许多游戏依赖叙事与追踪等级、NPC行为和后果模拟系统的结合；但将紧密编写的故事与深度模拟的世界相衔接（在沙盒和开放世界设定中最为突出）成本高昂。LLM驱动的世界开辟了新路径：单一框架即可协调数值状态、叙事声音、故事节奏和规则逻辑。实现这一点要求LLM系统维持持久化世界（谁在何处、刚刚发生了什么、当前事实为何），而当前部署的系统无法做到：叙事声音以自由散文形式断言状态却缺乏可验证表征，因此完全自主的游戏引擎仍不可行。我们将此视为架构选择而非语言模型局限，并报告一个进行中框架——交响现实——该框架将世界视为由单例编排智能体（类似桌游GM）拥有的规范化对象。我们将面向人类玩家的LLM驱动游戏世界形式化为参数化动作POMDP：状态为规范JSON实体构成的树，动作分解为$a=(k, x_k)$（离散意图类型加结构化JSON参数），智能体仅观测到状态的叙事投影$o=O(s)$，转移核$F$为LLM驱动的Plan-Diff-Validate-Apply (PDVA)管道，提交经模式验证和内容哈希的JSON差分。我们给出形式化模型、JSON状态示例、完整单轮示例，以及来自实际部署的15个说明性事件目录展示框架运作。通过计划中的人类玩家研究（结合多NPC并发代理和作为RL环境部署）进行的经验验证被定位为未来工作。