Creating scalable and believable game societies requires balancing authorial control with computational cost. Existing scripted NPC systems scale efficiently but are often rigid, whereas fully LLM-driven agents can produce richer social behavior at a much higher runtime cost. We present CASCADE, a three-layer architecture for low-cost, controllable social coordination in sandbox-style game worlds. A Macro State Director (Level 1) maintains discrete-time world-state variables and macro-level causal updates, while a modular Coordination Hub decomposes state changes through domain-specific components (e.g., professional and social coordination) and routes the resulting directives to tag-defined groups. Then Tag-Driven NPCs (Level 3) execute responses through behavior trees and local state/utility functions, invoking large language models only for on-demand player-facing interactions. We evaluate CASCADE through multiple micro-scenario prototypes and trace-based analysis, showing how a shared macro event can produce differentiated yet logically constrained NPC behaviors without per-agent prompting in the main simulation loop. CASCADE provides a modular foundation for scalable social simulation and future open-world authoring tools.

翻译：构建可扩展且可信的游戏社会需要在作者控制与计算成本之间取得平衡。现有的脚本化NPC系统虽能高效扩展，但往往僵硬刻板；而完全由大语言模型驱动的智能体虽能产生更丰富的社会行为，但运行时成本显著升高。我们提出CASCADE，一种用于沙盒类游戏世界中低成本、可控社会协调的三层架构。宏观状态导演器（第1层）维护离散时间的世界状态变量与宏观层面的因果更新，而模块化协调中枢通过领域特定组件（如职业与社会协调）分解状态变化，并将生成的指令路由至标签定义的群组。随后，标签驱动NPC（第3层）通过行为树与局部状态/效用函数执行响应，仅在与玩家交互时按需调用大语言模型。我们通过多个微场景原型与基于轨迹的分析对CASCADE进行评测，展示了一个共享宏观事件如何在无需对每个智能体进行逐个体提示的条件下，在主模拟循环中产生差异化但逻辑受约束的NPC行为。CASCADE为可扩展的社会模拟与未来的开放世界创作工具提供了模块化基础。