Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent 事件

Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent arXiv:2606.05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis. Supervised sequence models achieve strong accuracy but require task-specific training and offer limited decision-level transparency. Recent LLM-based methods improve interpretability, yet mostly rely on static prompts and single-pass inference, limitin