ARMove: Learning to Predict Human Mobility through Agentic Reasoning

ArXi:2604.17419v1 Announce Type: cross Human mobility prediction is a critical task but remains challenging due to its complexity and variability across populations and regions. Recently, large language models (LLMs) have made progress in zero-shot prediction, but existing methods suffer from limited interpretability (due to black-box reasoning), lack of iterative learning from new data, and poor transferability. In this paper, we