ActivityEditor: Learning to Synthesize Physically Valid Human Mobility
arXiv:2604.05529v1 Announce Type: new Abstract: 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
arXiv:2604.05529v1 Announce Type: new Abstract: 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.
Executive Summary
The article introduces ActivityEditor, an innovative dual-LLM-agent framework for zero-shot cross-regional human mobility trajectory synthesis, addressing critical data scarcity challenges in urban mobility modeling. By decomposing the task into intention-based and editor agents, the framework leverages demographic priors and reinforcement learning to enforce human mobility laws, ensuring socio-semantic coherence and physical validity. The approach achieves superior zero-shot performance across diverse urban contexts, demonstrating robustness in data-limited scenarios. While promising, the framework's reliance on reinforcement learning and LLM-agent collaboration introduces potential scalability and interpretability challenges, particularly in real-world deployment where explainability and computational efficiency are paramount.
Key Points
- ▸ ActivityEditor employs a dual-agent architecture to decompose complex mobility synthesis into intention generation and trajectory refinement stages.
- ▸ The framework incorporates demographic-driven priors and reinforcement learning with multi-dimensional rewards to enforce physical constraints and mobility regularities.
- ▸ Zero-shot transferability is validated through extensive experiments, demonstrating high statistical fidelity and physical validity across diverse urban contexts.
Merits
Innovative Dual-Agent Framework
The decomposition of mobility synthesis into intention-based and editor agents enables modular, scalable, and interpretable trajectory generation, addressing the limitations of monolithic models.
Reinforcement Learning for Physical Validity
The use of reinforcement learning with grounded rewards ensures adherence to real-world mobility laws, enhancing the fidelity of generated trajectories.
Zero-Shot Generalizability
The framework's ability to perform across diverse urban contexts without retraining addresses critical data scarcity challenges in mobility modeling.
Socio-Semantic Coherence
Demographic-driven priors and structured activity chains ensure generated intentions and trajectories align with real-world socio-demographic patterns.
Demerits
Computational Complexity
The dual-agent architecture and reinforcement learning framework may impose significant computational overhead, limiting scalability for large-scale urban applications.
Dependence on LLM Reliability
The framework's performance hinges on the accuracy and reliability of the underlying LLM agents, which may introduce errors or biases in intention generation or trajectory refinement.
Interpretability Challenges
The reinforcement learning and agent collaboration processes may lack transparency, complicating debugging and validation for real-world deployment.
Data Sufficiency for RL Training
While addressing data scarcity in inference, the reinforcement learning phase may require substantial ground-truth data for training, potentially limiting its applicability in extreme data-scarce scenarios.
Expert Commentary
ActivityEditor represents a paradigm shift in human mobility modeling by addressing the persistent challenge of data scarcity through a novel dual-agent framework. The integration of demographic priors and reinforcement learning to enforce physical constraints is both innovative and pragmatic, offering a robust solution for regions lacking historical trajectories. However, the framework's reliance on LLM agents and reinforcement learning introduces non-trivial challenges, particularly in terms of computational efficiency and interpretability. While the zero-shot transferability is a significant advantage, the potential for biases in intention generation or trajectory refinement—stemming from LLM limitations—warrants careful scrutiny. Furthermore, the ethical implications of generating synthetic mobility data in sensitive urban contexts cannot be overlooked. For policymakers and urban planners, ActivityEditor offers a powerful tool, but its deployment must be accompanied by rigorous validation and transparent governance frameworks to mitigate risks and ensure equitable application.
Recommendations
- ✓ Conduct third-party audits of the reinforcement learning framework to validate adherence to physical mobility laws and identify potential biases in intention generation.
- ✓ Develop modular, open-source implementations to facilitate integration with existing urban simulation platforms and encourage community-driven validation.
- ✓ Establish ethical guidelines and regulatory sandboxes for the use of synthetic mobility data, particularly in contexts involving vulnerable populations or sensitive geographic regions.
- ✓ Investigate hybrid approaches combining ActivityEditor with traditional statistical models to enhance interpretability and reduce computational overhead.
Sources
Original: arXiv - cs.AI