{"ID":3049904,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T15:44:26.945507316Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05130","arxiv_id":"2606.05130","title":"Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent","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, limiting their ability to seek additional evidence when mobility signals are weak or conflicting. We propose \\method{}, a training-free LLM-driven agent framework that formulates next-location prediction as adaptive evidence-controlled decision making. \\method{} resolves routine cases through a fast path based on historical regularity, while ambiguous cases trigger iterative tool use over recent trajectories, historical behavior, stay-move likelihood, and geographical evidence. Across three mobility datasets, AgentMob achieves the strongest overall performance among training-free LLM-based methods, with GPT-5.4 reaching 71.42\\% Acc@1 on BW, 33.14\\% on YJMob100K, and 33.50\\% on Shanghai ISP. On BW non-fast-path cases, the LLM controller improves Acc@1 from 30.65\\% to 48.62\\% over a same-tool statistical baseline, showing that its main benefit lies in resolving ambiguous predictions through adaptive evidence gathering. Our code is available at https://github.com/Unknown-zoo/AgentMob.","short_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 st...","url_abs":"https://arxiv.org/abs/2606.05130","url_pdf":"https://arxiv.org/pdf/2606.05130v1","authors":"[\"Linyao Chen\",\"Qinlao Zhao\",\"Zechen Li\",\"Mingming Li\",\"Likun Ni\",\"Jinyu Chen\",\"Yuhao Yao\",\"Xuan Song\",\"Noboru Koshizuka\",\"Hiroki Kobayashi\"]","published":"2026-06-03T17:34:01Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false,"code_links":[{"ID":612761,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-04T02:13:16.786527022Z","DeletedAt":null,"paper_id":3049904,"paper_url":"https://arxiv.org/abs/2606.05130","paper_title":"Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent","repo_url":"https://github.com/Unknown-zoo/AgentMob","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}