The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend. While MLLMs have been extensively studied for visual semantic understanding tasks, their ability to perform precise and quantitative spatial-temporal understanding in real-world applications remains largely unexamined, leading to uncertain prospects. To evaluate models' Spatial-Temporal Intelligence, we introduce STI-Bench, a benchmark designed to evaluate MLLMs' spatial-temporal understanding through challenging tasks such as estimating and predicting the appearance, pose, displacement, and motion of objects. Our benchmark encompasses a wide range of robot and vehicle operations across desktop, indoor, and outdoor scenarios. The extensive experiments reveals that the state-of-the-art MLLMs still struggle in real-world spatial-temporal understanding, especially in tasks requiring precise distance estimation and motion analysis.
View on arXiv@article{li2025_2503.23765,
title={ STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding? },
author={ Yun Li and Yiming Zhang and Tao Lin and XiangRui Liu and Wenxiao Cai and Zheng Liu and Bo Zhao },
journal={arXiv preprint arXiv:2503.23765},
year={ 2025 }
}