Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent
- Published
- Source
- arXiv
- Paper number
- 321
- Field
- Machine Learning
- arXiv ID
- 2606.05130
Key points
- Supervised sequence models achieve high accuracy, but they require task-specific training and provide little transparency at the decision level.
- This paper proposes a training-free LLM-driven agent framework that formulates next-location prediction as adaptive, evidence-controlled decision making.
- Across three mobility datasets, AgentMob achieves the strongest overall performance among training-free LLM-based methods, and GPT-5.4 reaches 71.42 percent Acc@1 on BW, 33.14 percent on YJMob100K, and 33.50 percent on Shanghai ISP.
Paper links
External research summaries. These are not HDATF publications or measured product results.