Add task category and links to paper, code, and project page

Hi, I'm Niels from the Hugging Face community science team.

This PR improves the dataset card for NKIBench by:
- Adding the `text-generation` task category to the YAML metadata.
- Including links to the associated research paper, the official GitHub repository, and the project page.
- Ensuring the citation is clearly presented.

These changes help researchers and developers better discover the context and implementation details of the dataset.

README.md

@@ -1,7 +1,12 @@
 ---
-license: apache-2.0
 language:
 - en
+license: apache-2.0
+size_categories:
+- n<1K
+pretty_name: NKIBench
+task_categories:
+- text-generation
 tags:
 - nki
 - aws-neuron
@@ -9,15 +14,16 @@ tags:
 - kernel
 - benchmark
 - environment
-pretty_name: NKIBench
-size_categories:
-- n<1K
 viewer: false
 ---
 
 # NKIBench
 
-NKIBench is a benchmark of AWS [Neuron Kernel Interface (NKI)](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/nki/index.html) kernels paired with NumPy reference implementations. Each task provides a specification, a ground-truth NumPy forward pass, and an optimized NKI kernel targeting AWS Trainium devices, together with tooling to compile, check numerical correctness, and measure on-device latency. [Paper](https://arxiv.org/pdf/2511.15915).
+NKIBench is a benchmark of AWS [Neuron Kernel Interface (NKI)](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/nki/index.html) kernels paired with NumPy reference implementations. It was introduced as part of the paper [AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization](https://huggingface.co/papers/2511.15915).
+
+[Project Page](https://ppl.stanford.edu/accelopt.html) | [Paper](https://huggingface.co/papers/2511.15915) | [GitHub](https://github.com/zhang677/AccelOpt)
+
+Each task provides a specification, a ground-truth NumPy forward pass, and an optimized NKI kernel targeting AWS Trainium devices, together with tooling to compile, check numerical correctness, and measure on-device latency.
 
 ## Dataset structure
 
@@ -112,14 +118,13 @@ print("latency :", result.metadata.get("latency"), "ms")
 
 ## Citation
 
-If you use NKIBench in your work, please cite the repository.
+If you use NKIBench in your work, please cite the paper:
 
 ```bibtex
-@article{zhang2026accelopt, 
-  title={AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization}, 
-  author={Zhang, Genghan and Zhu, Shaowei and Wei, Anjiang and Song, Zhenyu and Nie, Allen and Jia, Zhen and Vijaykumar, Nandita and Wang, Yida and Olukotun, Kunle}, 
-  journal={Proceedings of Machine Learning and Systems}, 
-  volume={9}, 
-  year={2026} 
- }
-```
+@article{zhang2025accelopt,
+  title={AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization},
+  author={Zhang, Genghan and Zhu, Shaowei and Wei, Anjiang and Song, Zhenyu and Nie, Allen and Jia, Zhen and Vijaykumar, Nandita and Wang, Yida and Olukotun, Kunle},
+  journal={arXiv preprint arXiv:2511.15915},
+  year={2025}
+}
+```