Update README.md

README.md

@@ -19,6 +19,12 @@ pipeline_tag: visual-question-answering
 
 
 ## Introduction
+We present VideoScore2, a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality, text-to-video alignment, and physical/common-sense consistency while producing detailed chain-of-thought rationales.
+
+Our model is trained on a large-scale dataset VideoFeedback2 containing 27,168 human-annotated videos with both scores and reasoning traces across three dimensions, using a two-stage pipeline of supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO) to enhance analytical robustness.
+
+Extensive experiments demonstrate that VideoScore2 achieves superior performance with 44.35 (+5.94) accuracy on our in-domain benchmark VideoScore-Bench-v2 and 50.37 (+4.32) average performance across four out-of-domain benchmarks (VideoGenReward-Bench, VideoPhy2, etc).
+
 
 ## Usage
 
@@ -193,6 +199,7 @@ see [VideoScore2/training](https://github.com/TIGER-AI-Lab/VideoScore2/tree/main
 see [VideoScore2/evaluation](https://github.com/TIGER-AI-Lab/VideoScore2/tree/main/eval) for details
 
 ## Citation
+```bibtex
 @misc{he2025videoscore2thinkscoregenerative,
       title={VideoScore2: Think before You Score in Generative Video Evaluation}, 
       author={Xuan He and Dongfu Jiang and Ping Nie and Minghao Liu and Zhengxuan Jiang and Mingyi Su and Wentao Ma and Junru Lin and Chun Ye and Yi Lu and Keming Wu and Benjamin Schneider and Quy Duc Do and Zhuofeng Li and Yiming Jia and Yuxuan Zhang and Guo Cheng and Haozhe Wang and Wangchunshu Zhou and Qunshu Lin and Yuanxing Zhang and Ge Zhang and Wenhao Huang and Wenhu Chen},
@@ -201,4 +208,5 @@ see [VideoScore2/evaluation](https://github.com/TIGER-AI-Lab/VideoScore2/tree/ma
       archivePrefix={arXiv},
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2509.22799}, 
-}
+}
+```