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ActivityEditor, a dual-LLM-agent framework, tackles zero-shot cross-regional human trajectory generation by decomposing the task into intention generation and trajectory refinement. An intention-based agent creates structured human intentions and activity chains using demographic priors, while an editor agent iteratively refines these into mobility trajectories. The editor agent learns to enforce human mobility laws through reinforcement learning with rewards based on real-world physical constraints, ensuring high-fidelity trajectory generation.
Synthesizing realistic human mobility in data-scarce regions is now possible thanks to a dual-LLM-agent framework that learns physical constraints via reinforcement learning.
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.
Tsinghua AI