LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social context. However, full multi-round simulation scales linearly with both population size and horizon, requiring every agent to query the LLM at every round. We propose Adaptive Prototype Simulation (APS), a framework that reframes scalable LLM-based simulation as a recurrent oracle-allocation problem. APS retains the designated LLM as the online transition oracle while querying adaptive core prototypes, selected singleton-tail agents, and shadow-audit agents. Prototype responses induce local response surfaces for nearby agents, reducing online LLM calls without replacing the underlying transition model. To control approximation bias, shadow-audit residual correction estimates propagation residuals for aggregate correction and future budget allocation, while tail-protected singleton routing directly queries selected isolated, heterogeneous, or high-curvature regions that are vulnerable to smoothing. Theoretically, we treat APS as an estimator for full-scale high-precision individual social simulation and decompose its errors into prototype-coverage error, shadow-audit residual-correction error, local-propagation bias, and temporal context mismatch. Under the reported protocols, APS gives lower reference-aligned distributional discrepancy than scale-oriented and same-budget baselines while reducing online LLM calls, with ablations and compact robustness checks diagnosing the main bias-control mechanisms. In a 10M-agent, multi-round public-opinion simulation, APS achieves a 381.1-fold reduction over full simulation, with reference-aligned final-round JSD of 0.094 against the corresponding full-LLM reference.

翻译：基于大语言模型（LLM）的智能体模拟为研究依赖于场景事件、记忆、人口统计学特征及演化社会情境的群体响应轨迹提供了灵活的计算工具。然而，完整的多轮模拟在群体规模与时间跨度上呈线性增长，要求每个智能体在每轮迭代中均需查询LLM。本文提出自适应原型模拟（APS）框架，将可扩展的LLM模拟重新定义为循环预言分配问题。APS保留指定LLM作为在线转移预言，同时查询自适应核心原型、选定的单值尾智能体及影子审计智能体。原型响应为邻近智能体构建局部响应曲面，在不替换底层转移模型的前提下减少在线LLM调用。为控制近似偏差，影子审计残差校正可估计传播残差用于全局校正和未来预算分配，而尾保护单值路由直接查询易受平滑影响的孤立、异质或高曲率区域。理论上，我们将APS视为全尺度高精度个体社会模拟的估计器，并将其误差分解为原型覆盖误差、影子审计残差校正误差、局部传播偏差及时间上下文失配。在既定协议下，APS相较于面向规模与同等预算的基线方法，不仅实现了更低的参考对齐分布差异，还减少了在线LLM调用次数，并通过消融实验与紧凑鲁棒性检验验证了主要偏差控制机制。在包含1000万智能体、多轮次公共舆论模拟场景中，APS相较于完整模拟实现381.1倍缩减，最终轮次参考对齐JSD为0.094（对应完整LLM参考）。