Add paper link, GitHub repository, and update metadata

README.md

@@ -1,20 +1,24 @@
 ---
 language:
 - en
+license: mit
+task_categories:
+- image-text-to-text
+- visual-question-answering
 tags:
 - vision-language
 - vqa
 - diagnostic-benchmark
 - xai
-license: mit
-task_categories:
-- visual-question-answering
 ---
 
 # GridVQA-X Datasets
 
 GridVQA-X is the first diagnostic framework designed to objectively evaluate the faithfulness of post-hoc cross-modal explainers. By utilizing a closed-world synthesis logic with mathematically guaranteed unique ground-truth explanations, it provides a controlled testbed to isolate genuine cross-modal spatial reasoning from shallow shortcuts.
 
+- **Paper:** [GridVQA-X: A Framework for Evaluating Multimodal Explainability Methods](https://huggingface.co/papers/2606.14740)
+- **Repository:** [GitHub - AikyamLab/grid-vqax](https://github.com/AikyamLab/grid-vqax)
+
 ## Dataset Summary
 The dataset features **S × S** visual grids populated by geometric objects, paired with multi-hop spatial reasoning queries. Each query defines a **Target** object to find/count and one or more **Anchor** reference objects connected by directional tokens.
 
@@ -35,4 +39,20 @@ Every sample is parameterized by a strict 4-tuple configuration: **(Depth, QType
 *   **Density (d_0.3 or d_0.7):** Adjusts grid object count to test spatial localization fidelity against background noise.
 
 ## Ground-Truth Explanations
-The dataset provides a mathematically proven, unique ground-truth causal explanation. The ground-truth visual masks are strictly bounded to the target and anchor items (**A ∪ T**), leaving all other distractor objects with a causal effect of exactly zero.
+The dataset provides a mathematically proven, unique ground-truth causal explanation. The ground-truth visual masks are strictly bounded to the target and anchor items (**A ∪ T**), leaving all other distractor objects with a causal effect of exactly zero.
+
+## Citation
+
+If you use GridVQA-X in your research, please cite:
+
+```bibtex
+@misc{belsare2026gridvqaxframeworkevaluatingmultimodal,
+      title={GridVQA-X: A Framework for Evaluating Multimodal Explainability Methods}, 
+      author={Sujay Belsare and Sudarshan Nikhil and Sushant Kumar and Ponnurangam Kumaraguru and Chirag Agarwal},
+      year={2026},
+      eprint={2606.14740},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2606.14740}, 
+}
+```