Add task category and update paper link

README.md

@@ -1,3 +1,10 @@
+---
+language:
+- en
+task_categories:
+- video-text-to-text
+---
+
 <!-- # TimeBlind Benchmark -->
 
 <!-- TimeBlind: A video VQA benchmark for evaluating temporal understanding in vision-language models -->
@@ -12,10 +19,13 @@
 
 <div align="center">
 
-[🏠**Home Page**](https://baiqi-li.github.io/timeblind_project/) | [🤗**HuggingFace**](https://huggingface.co/datasets/BaiqiL/TimeBlind) | [**📖Paper**(coming soon)](https://arxiv.org/abs/2602.00288) | [🖥️ **Code**](https://github.com/Baiqi-Li/TimeBlind)
+[🏠**Home Page**](https://baiqi-li.github.io/timeblind_project/) | [🤗**HuggingFace**](https://huggingface.co/datasets/BaiqiL/TimeBlind) | [**📖Paper**](https://huggingface.co/papers/2602.00288) | [🖥️ **Code**](https://github.com/Baiqi-Li/TimeBlind)
 
 </div>
 
+## Introduction
+Fine-grained spatio-temporal understanding is essential for video reasoning and embodied AI. TimeBlind is a diagnostic benchmark for compositional spatio-temporal understanding. Inspired by cognitive science, TimeBlind categorizes fine-grained temporal understanding into three levels: recognizing atomic events, characterizing event properties, and reasoning about event interdependencies. It leverages a minimal-pairs paradigm where video pairs share identical static visual content but differ solely in temporal structure, utilizing complementary questions to neutralize language priors.
+
 ## Setup
 
 ```bash
@@ -71,7 +81,7 @@ I-Acc serves as our primary metric.
 - **Acc**: Binary VQA accuracy
 - **Q_Acc**: Question accuracy
 - **V_Acc**: Video accuracy
-- **I_Acc**: Instance accuracy (the primary metric in our pape)
+- **I_Acc**: Instance accuracy (the primary metric in our paper)
 
 # Copyright & Infringement Notice
 The data provided in this benchmark is intended for academic research purposes only. We respect the intellectual property rights of the content creators.