@inproceedings{zhao-etal-2025-cityeqa,
    title = "{C}ity{EQA}: A Hierarchical {LLM} Agent on Embodied Question Answering Benchmark in City Space",
    author = "Zhao, Yong  and
      Xu, Kai  and
      Zhu, Zhengqiu  and
      Hu, Yue  and
      Zheng, Zhiheng  and
      Chen, Yingfeng  and
      Ji, Yatai  and
      Gao, Chen  and
      Li, Yong  and
      Huang, Jincai",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.630/",
    pages = "12476--12491",
    ISBN = "979-8-89176-332-6",
    abstract = "Embodied Question Answering (EQA) has primarily focused on indoor environments, leaving the complexities of urban settings{---}spanning environment, action, and perception{---}largely unexplored. To bridge this gap, we introduce CityEQA, a new task where an embodied agent answers open-vocabulary questions through active exploration in dynamic city spaces. To support this task, we present CityEQA-EC, the first benchmark dataset featuring 1,412 human-annotated tasks across six categories, grounded in a realistic 3D urban simulator. Moreover, we propose -Manager-Actor (PMA), a novel agent tailored for CityEQA. PMA enables long-horizon planning and hierarchical task execution: the Planner breaks down the question answering into sub-tasks, the Manager maintains an object-centric cognitive map for spatial reasoning during the process control, and the specialized Actors handle navigation, exploration, and collection sub-tasks. Experiments demonstrate that PMA achieves 60.7{\%} of human-level answering accuracy, significantly outperforming frontier-based baselines. While promising, the performance gap compared to humans highlights the need for enhanced visual reasoning in CityEQA. This work paves the way for future advancements in urban spatial intelligence. Dataset and code are available at https://github.com/tsinghua-fib-lab/CityEQA.git."
}