Update metadata and improve model card

README.md

@@ -1,26 +1,27 @@
 ---
 base_model:
 - Wan-AI/Wan2.2-I2V-A14B-Diffusers
+library_name: diffusers
 license: apache-2.0
-library_name: transformers
+pipeline_tag: image-to-video
 ---
 
 # VBVR: A Very Big Video Reasoning Suite
 
 <a href="https://video-reason.com" target="_blank">
-    <img alt="Code" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
+    <img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
 </a>
-<a href="https://github.com/orgs/Video-Reason/repositories" target="_blank">
+<a href="https://github.com/Video-Reason/VBVR-EvalKit" target="_blank">
     <img alt="Code" src="https://img.shields.io/badge/VBVR-Code-100000?style=flat-square&logo=github&logoColor=white" height="20" />
 </a>
-  <a href="https://arxiv.org/abs/2602.20159" target="_blank">
-      <img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR-red?logo=arxiv" height="20" />
-  </a>
+<a href="https://huggingface.co/papers/2602.20159" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR-red?logo=arxiv" height="20" />
+</a>
 <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Dataset" target="_blank">
-    <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Dataset-Data-ffc107?color=ffc107&logoColor=white" height="20" />
+    <img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Dataset-Data-ffc107?color=ffc107&logoColor=white" height="20" />
 </a>
 <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data" target="_blank">
-    <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Bench-Data-ffc107?color=ffc107&logoColor=white" height="20" />
+    <img alt="Bench Data" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Bench-Data-ffc107?color=ffc107&logoColor=white" height="20" />
 </a>
 <a href="https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard" target="_blank">
     <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Bench-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
@@ -31,13 +32,16 @@ library_name: transformers
 Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, 
 enabling intuitive reasoning over motion, interaction, and causality. Rapid progress in video models has focused primarily on visual quality. 
 Systematically studying video reasoning and its scaling behavior suffers from a lack of video reasoning (training) data. 
+
 To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks 
 and over one million video clips—approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, 
 a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, 
 enabling reproducible and interpretable diagnosis of video reasoning capabilities. 
+
 Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization 
 to unseen reasoning tasks. **Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning.**
 
+The model was presented in the paper [A Very Big Video Reasoning Suite](https://huggingface.co/papers/2602.20159).
 
 <table>
     <tr>
@@ -135,7 +139,7 @@ to unseen reasoning tasks. **Together, VBVR lays a foundation for the next stage
 </table>
 
 ## Release Information
-VBVR-Wan2.2 is trained from Wan2.2-I2V-A14B without architectural modifications, as the goal of VBVR-Wan2.2 is to *investigate data scaling behavior* and provide a *strong baseline model* for the video reasoning research community. Leveraging the VBVR-Dataset, which to our knowledge constitutes one of the largest video reasoning datasets to date, VBVR-Wan2.2 achieved highest score on VBVR-Bench.
+VBVR-Wan2.2 is trained from Wan2.2-I2V-A14B without architectural modifications, as the goal of VBVR-Wan2.2 is to *investigate data scaling behavior* and provide a *strong baseline model* for the video reasoning research community. Leveraging the VBVR-Dataset, which constitutes one of the largest video reasoning datasets to date, VBVR-Wan2.2 achieved highest score on VBVR-Bench.
 
 In this release, we present 
 [**VBVR-Wan2.2**](https://huggingface.co/Video-Reason/VBVR-Wan2.2), 
@@ -168,11 +172,26 @@ python example.py \
 
 ## 🖊️ Citation
 
-```bib
+```bibtex
 @article{vbvr2026,
-      title={A Very Big Video Reasoning Suite}, 
-      author={Maijunxian Wang and Ruisi Wang and Juyi Lin and Ran Ji and Thaddäus Wiedemer and Qingying Gao and Dezhi Luo and Yaoyao Qian and Lianyu Huang and Zelong Hong and Jiahui Ge and Qianli Ma and Hang He and Yifan Zhou and Lingzi Guo and Lantao Mei and Jiachen Li and Hanwen Xing and Tianqi Zhao and Fengyuan Yu and Weihang Xiao and Yizheng Jiao and Jianheng Hou and Danyang Zhang and Pengcheng Xu and Boyang Zhong and Zehong Zhao and Gaoyun Fang and John Kitaoka and Yile Xu and Hua Xu and Kenton Blacutt and Tin Nguyen and Siyuan Song and Haoran Sun and Shaoyue Wen and Linyang He and Runming Wang and Yanzhi Wang and Mengyue Yang and Ziqiao Ma and Raphaël Millière and Freda Shi and Nuno Vasconcelos and Daniel Khashabi and Alan Yuille and Yilun Du and Ziming Liu and Bo Li and Dahua Lin and Ziwei Liu and Vikash Kumar and Yijiang Li and Lei Yang and Zhongang Cai and Hokin Deng},
+  title   = {A Very Big Video Reasoning Suite},
+  author  = {Wang, Maijunxian and Wang, Ruisi and Lin, Juyi and Ji, Ran and
+             Wiedemer, Thadd{\"a}us and Gao, Qingying and Luo, Dezhi and
+             Qian, Yaoyao and Huang, Lianyu and Hong, Zelong and Ge, Jiahui and
+             Ma, Qianli and He, Hang and Zhou, Yifan and Guo, Lingzi and
+             Mei, Lantao and Li, Jiachen and Xing, Hanwen and Zhao, Tianqi and
+             Yu, Fengyuan and Xiao, Weihang and Jiao, Yizheng and
+             Hou, Jianheng and Zhang, Danyang and Xu, Pengcheng and
+             Zhong, Boyang and Zhao, Zehong and Fang, Gaoyun and Kitaoka, John and
+             Xu, Yile and Xu, Hua bureau and Blacutt, Kenton and Nguyen, Tin and
+             Song, Siyuan and Sun, Haoran and Wen, Shaoyue and He, Linyang and
+             Wang, Runming and Wang, Yanzhi and Yang, Mengyue and Ma, Ziqiao and
+             Milli{\`e}re, Rapha{\"e}l and Shi, Freda and Vasconcelos, Nuno and
+             Khashabi, Daniel and Yuille, Alan and Du, Yilun and Liu, Ziming and
+             Lin, Dahua and Liu, Ziwei and Kumar, Vikash and Li, Yijiang and
+             Yang, Lei and Cai, Zhongang and Deng, Hokin},
   journal = {arXiv preprint arXiv:2602.20159},
-  year = {2026}
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2602.20159}
 }
 ```