Metaphorical videos are prevalent across various real-world scenarios to convey complex ideas, and understanding them typically requires high-order cognitive capabilities. The lack of systematic studies on metaphorical video understanding not only constrains the real-world applicability of MLLMs but also impedes the thorough assessment of their high-order cognitive capabilities. To bridge this gap, we propose MetaphorVU-Bench, the first systematic and comprehensive benchmark dedicated to metaphorical video understanding. Through experiments, we find current MLLMs struggle with accurate metaphorical video understanding, lagging far behind human level, primarily due to defective cross-domain mapping. Motivated by this finding, we construct a metaphor knowledge graph as mapping augmentation and propose MetaphorBoost, an inference-time enhancement framework achieving consistent performance improvement. Our benchmark, analysis, and method provide useful insights and a foundation for future research on advancing MLLMs.
@article{arxiv.2605.25461,
title = {MetaphorVU: Towards Metaphorical Video Understanding},
author = {Zhuoqun Li and Boxi Cao and Guiping Jiang and Fangrui Lv and Ruotong Pan and Jianan Wang and Xiangyu Wu and Hongyu Lin and Yaojie Lu and Yong Du and Ruyin Jia and Liyan and Tingting Gao and Han Li and Xianpei Han and Le Sun},
journal= {arXiv preprint arXiv:2605.25461},
year = {2026}
}