Update README.md

README.md

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 ## Introduction
 
 CompactDS is a diverse, high-quality, web-scale datastore that achieves high retrieval accuracy and subsecond latency on a single-node deployment, making it suitable for academic use. Its core design combines a compact set of high-quality, diverse data sources with in-memory approximate nearest neighbor (ANN) retrieval and on-disk exact search. We release CompactDS and our retrieval pipeline as a fully reproducible alternative to commercial search, supporting future research exploring retrieval-based AI systems. Check out our paper, [Frustratingly Simple Retrieval Improves
-Challenging, Reasoning-Intensive Benchmarks](TODO), for full details.  
+Challenging, Reasoning-Intensive Benchmarks](http://arxiv.org/abs/2507.01297), for full details.  
 
 Due to data sensitivity issues, we release a version of CompactDS **excluding Textbook and Books**. We also use an \# subquantizer of 64 (instead of 256 used in the paper) to build an 102GB ANN index. The full collection of the released assets is as following:
 - [CompactDS-102GB ](https://huggingface.co/datasets/alrope/CompactDS-102GB) (this dataset): the Faiss IVFPQ index and chuncked passages.
@@ -62,5 +62,10 @@ As we adopt Inverted File Product Quantization (IVFPQ) index that approximate th
 
 ## Citation
 ```
-TODO
+@article{lyu2025compactds,
+  title={Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks},
+  author={Xinxi Lyu and Michael Duan and Rulin Shao and Pang Wei Koh and Sewon Min}
+  journal={arXiv preprint arXiv:2507.01297},
+  year={2025}
+}
 ```