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[2602.16727] Mobility-Aware Cache Framework for Scalable LLM-Based Human Mobility Simulation
Summary
The paper presents a Mobility-Aware Cache Framework (MobCache) designed to enhance the efficiency of large-scale human mobility simulations using large language models (LLMs).
Why It Matters
This research addresses the computational challenges associated with simulating human mobility, which is vital for urban planning and transportation analysis. By improving simulation efficiency without sacrificing performance, it has significant implications for various applications in AI and urban studies.
Key Takeaways
- MobCache leverages reconstructible caches for efficient mobility simulations.
- The framework includes a reasoning component that encodes reasoning steps as latent-space embeddings.
- A lightweight decoder translates these embeddings into natural language, enhancing simulation fidelity.
- Experiments demonstrate significant efficiency improvements while maintaining state-of-the-art performance.
- The research contributes to the scalability of LLM applications in real-world scenarios.
Computer Science > Artificial Intelligence arXiv:2602.16727 (cs) [Submitted on 17 Feb 2026] Title:Mobility-Aware Cache Framework for Scalable LLM-Based Human Mobility Simulation Authors:Hua Yan, Heng Tan, Yingxue Zhang, Yu Yang View a PDF of the paper titled Mobility-Aware Cache Framework for Scalable LLM-Based Human Mobility Simulation, by Hua Yan and Heng Tan and Yingxue Zhang and Yu Yang View PDF HTML (experimental) Abstract:Large-scale human mobility simulation is critical for applications such as urban planning, epidemiology, and transportation analysis. Recent works treat large language models (LLMs) as human agents to simulate realistic mobility behaviors using structured reasoning, but their high computational cost limits scalability. To address this, we design a mobility-aware cache framework named MobCache that leverages reconstructible caches to enable efficient large-scale human mobility simulations. It consists of: (1) a reasoning component that encodes each reasoning step as a latent-space embedding and uses a latent-space evaluator to enable the reuse and recombination of reasoning steps; and (2) a decoding component that employs a lightweight decoder trained with mobility law-constrained distillation to translate latent-space reasoning chains into natural language, thereby improving simulation efficiency while maintaining fidelity. Experiments show that MobCache significantly improves efficiency across multiple dimensions while maintaining performance compa...
