YJMob100K

AgentMob: Training-free LLM agent framework for evidence-grounded mobility prediction

AgentMob is a training-free LLM-driven agent framework that formulates next-location prediction as adaptive evidence-controlled decision making, using a fast path for routine cases and iterative tool use for ambiguous ones. Evaluated on three mobility datasets, it achieves the strongest overall performance among training-free LLM-based methods, with GPT-5.4 reaching 71.42% Acc@1 on the BW dataset. The framework demonstrates that LLM controllers add most value in resolving ambiguous predictions through adaptive evidence gathering rather than routine cases.