We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and itinerary planning. STAgent is a specialized model capable of interacting with ten distinct tools within spatio-temporal scenarios, enabling it to explore, verify, and refine intermediate steps during complex reasoning. Notably, STAgent effectively preserves its general capabilities. We empower STAgent with these capabilities through three key contributions: (1) a stable tool environment that supports over ten domain-specific tools, enabling asynchronous rollout and training; (2) a hierarchical data curation framework that identifies high-quality data like a needle in a haystack, curating high-quality queries by retaining less than 1\% of the raw data, emphasizing both diversity and difficulty; and (3) a cascaded training recipe that starts with a seed SFT stage acting as a guardian to measure query difficulty, followed by a second SFT stage fine-tuned on queries with high certainty, and an ultimate RL stage that leverages data of low certainty. Initialized with Qwen3-30B-A3B to establish a strong SFT foundation and leverage insights into sample difficulty, STAgent yields promising performance on TravelBench while maintaining its general capabilities across a wide range of general benchmarks, thereby demonstrating the effectiveness of our proposed agentic model.
@article{arxiv.2512.24957,
title = {AMAP Agentic Planning Technical Report},
author = {AMAP AI Agent Team and Yulan Hu and Xiangwen Zhang and Sheng Ouyang and Hao Yi and Lu Xu and Qinglin Lang and Lide Tan and Xiang Cheng and Tianchen Ye and Zhicong Li and Ge Chen and Wenjin Yang and Zheng Pan and Shaopan Xiong and Siran Yang and Ju Huang and Yan Zhang and Jiamang Wang and Yong Liu and Yinfeng Huang and Ning Wang and Tucheng Lin and Xin Li and Ning Guo},
journal= {arXiv preprint arXiv:2512.24957},
year = {2026}
}