Populate ImplicitQA dataset card content and links

This PR populates the `README.md` content for the ImplicitQA dataset, providing a detailed description of its purpose, composition, and the implicit reasoning challenges it addresses, derived from the paper abstract. It includes links to the associated Hugging Face paper page ([https://huggingface.co/papers/2506.21742](https://huggingface.co/papers/2506.21742)) and the project page ([https://implicitqa.github.io/](https://implicitqa.github.io/)).

Additionally, it adds the `implicit-reasoning` tag to the dataset metadata for improved discoverability and categorization within the Hugging Face Hub.

README.md

@@ -1,15 +1,38 @@
 ---
-license: mit
-task_categories:
-- video-text-to-text
 language:
 - en
-pretty_name: ImplicitQA
+license: mit
 size_categories:
 - 1K<n<10K
+task_categories:
+- video-text-to-text
+pretty_name: ImplicitQA
 configs:
 - config_name: default
   data_files:
   - split: eval
     path: ImplicitQAv0.1.2.jsonl
----
+tags:
+- implicit-reasoning
+---
+
+# ImplicitQA Dataset
+
+The ImplicitQA dataset was introduced in the paper [ImplicitQA: Going beyond frames towards Implicit Video Reasoning](https://huggingface.co/papers/2506.21742).
+
+**Project page:** https://implicitqa.github.io/
+
+ImplicitQA is a novel benchmark specifically designed to test models on **implicit reasoning** in Video Question Answering (VideoQA). Unlike existing VideoQA benchmarks that primarily focus on questions answerable through explicit visual content (actions, objects, events directly observable within individual frames or short clips), ImplicitQA addresses the need for models to infer motives, causality, and relationships across discontinuous frames. This mirrors human-like understanding of creative and cinematic videos, which often employ storytelling techniques that deliberately omit certain depictions.
+
+The dataset comprises 1,000 meticulously annotated QA pairs derived from over 320 high-quality creative video clips. These QA pairs are systematically categorized into key reasoning dimensions, including:
+
+*   Lateral and vertical spatial reasoning
+*   Depth and proximity
+*   Viewpoint and visibility
+*   Motion and trajectory
+*   Causal and motivational reasoning
+*   Social interactions
+*   Physical context
+*   Inferred counting
+
+The annotations are deliberately challenging, crafted to ensure high quality and to highlight the difficulty of implicit reasoning for current VideoQA models. By releasing both the dataset and its data collection framework, the authors aim to stimulate further research and development in this crucial area of AI.