Warehouse Spatial Question Answering with LLM Agent: 1st Place Solution of the 9th AI City Challenge Track 3

Hsiang-Wei Huang, Pyongkun Kim, Jen-Hao Cheng, Kuang-Ming Chen, Cheng-Yen Yang, Bahaa Alattar, Yi-Ru Lin, Sangwon Kim, Kwangju Kim, Chung-I Huang, Jenq-Neng Hwang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 5283-5287

Spatial understanding has been a challenging task for existing Multi-modal Large Language Models (MLLMs). Previous methods leverage large-scale MLLM fine-tuning to enhance MLLM's spatial understanding ability. In this paper, we present a data-efficient approach. We propose a LLM agent system with strong and advanced spatial reasoning ability, which can be used to solve the challenging spatial question answering task in complex indoor warehouse scenarios. Our system integrates multiple tools that allow the LLM agent to conduct spatial reasoning and API tools interaction to answer the given complicated spatial question. Extensive evaluations on the 2025 AI City Challenge Physical AI Spatial Intelligence Warehouse dataset demonstrate that our system achieves high accuracy and efficiency in tasks such as object retrieval, counting, and distance estimation. The code is available at: https://github.com/hsiangwei0903/SpatialAgent.