人类移动建模对多种城市应用至关重要。然而，现有数据驱动方法常受限于数据稀缺问题，难以在缺乏或无法获取历史轨迹数据的区域中应用。为弥补这一空白，我们提出\textbf{ActivityEditor}——一种面向零样本跨区域轨迹生成的新型双大语言模型（LLM）代理框架。该框架将复杂的轨迹合成任务分解为两个协同阶段：首先，一个基于意图的代理利用人口统计学驱动的先验知识，生成结构化的人类意图与粗粒度活动链，以保障高层级的社会语义一致性；随后，编辑代理通过迭代修订，将上述输出精炼为符合人类移动规律的轨迹。该能力通过强化学习获得，其奖励函数综合了多个基于真实世界物理约束的指标，使代理能够内化移动规律并确保高保真度的轨迹生成。大量实验表明，\textbf{ActivityEditor}在跨不同城市场景迁移时展现出卓越的零样本性能，同时保持高度的统计保真性与物理有效性，为数据稀缺场景下的移动仿真提供了鲁棒且高度可泛化的解决方案。代码地址：https://anonymous.4open.science/r/ActivityEditor-066B。

Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limiting their applicability in regions where historical trajectories are unavailable or restricted. To bridge this gap, we propose \textbf{ActivityEditor}, a novel dual-LLM-agent framework designed for zero-shot cross-regional trajectory generation. Our framework decomposes the complex synthesis task into two collaborative stages. Specifically, an intention-based agent, which leverages demographic-driven priors to generate structured human intentions and coarse activity chains to ensure high-level socio-semantic coherence. These outputs are then refined by editor agent to obtain mobility trajectories through iteratively revisions that enforces human mobility law. This capability is acquired through reinforcement learning with multiple rewards grounded in real-world physical constraints, allowing the agent to internalize mobility regularities and ensure high-fidelity trajectory generation. Extensive experiments demonstrate that \textbf{ActivityEditor} achieves superior zero-shot performance when transferred across diverse urban contexts. It maintains high statistical fidelity and physical validity, providing a robust and highly generalizable solution for mobility simulation in data-scarce scenarios. Our code is available at: https://anonymous.4open.science/r/ActivityEditor-066B.