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README.md

@@ -10,8 +10,8 @@ configs:
         path: VKnowU.json
 ---
 
-# VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs <a href="https://arxiv.org/abs/2511.20272">ArXiv</a>
-![Overview of ExpVid](figs/VKnowU.png)
+# VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs <a href="https://arxiv.org/abs/2511.20272">  📖ArXiv</a>
+![📖](figs/VKnowU.png)
 
 While Multimodal Large Language Models (MLLMs) have become adept at recognizing objects, they often lack the intuitive, human-like understanding of the world's underlying physical and social principles. This high-level vision-grounded semantics, which we term visual knowledge, forms a bridge between perception and reasoning, yet remains an underexplored area in current MLLMs.
 
@@ -38,5 +38,10 @@ To systematically evaluate this capability, we present [📊VKnowU](https://hugg
 If you find this work useful for your research, please consider citing VKnowU. Your acknowledgement would greatly help us in continuing to contribute resources to the research community.
 
 ```
-coming soon
+@article{jiang2025vknowu,
+  title={VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs},
+  author={Jiang, Tianxiang and Xia, Sheng and Xu, Yicheng and Wu, Linquan and Zeng, Xiangyu and Wang, Limin and Qiao, Yu and Wang, Yi},
+  journal={arXiv preprint arXiv:2511.20272},
+  year={2025}
+}
 ```