Update README.md

README.md

@@ -25,8 +25,8 @@ configs:
 ## Dataset Overview
 FreshRetailNet-50K is the first large-scale benchmark for censored demand estimation in the fresh retail domain, **incorporating approximately 20% organically occurring stockout data**. It comprises 50,000 store-product 90-day time series of detailed hourly sales data from 898 stores in 18 major cities, encompassing 863 perishable SKUs with meticulous stockout event annotations. The hourly stock status records unique to this dataset, combined with rich contextual covariates including promotional discounts, precipitation, and other temporal features, enable innovative research beyond existing solutions.
 
-- [Technical Report](It will be posted later.) - Discover the methodology and technical details behind FreshRetailNet-50K.
-- [Github Repo](It will be posted later.) - Access the complete pipeline used to train and evaluate.
+- [Technical Report](https://arxiv.org/abs/2505.16319) - Discover the methodology and technical details behind FreshRetailNet-50K.
+- [Github Repo](https://github.com/Dingdong-Inc/frn-50k-baseline) - Access the complete pipeline used to train and evaluate.
 
 This dataset is ready for commercial/non-commercial use.
 
@@ -113,11 +113,11 @@ If you find the data useful, please cite:
 ```
 @article{2025freshretailnet-50k,
       title={FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail},
-      author={Anonymous Author(s)},
+      author={Yangyang Wang, Jiawei Gu, Li Long, Xin Li, Li Shen, Zhouyu Fu, Xiangjun Zhou, Xu Jiang},
       year={2025},
-      eprint={2505.xxxxx},
+      eprint={2505.16319},
       archivePrefix={arXiv},
-      primaryClass={stat.ML},
-      url={https://arxiv.org/abs/2505.xxxxx},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2505.16319},
 }
 ```