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README.md

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 # VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting
 
-This is the official pretrained model weights for the paper [arXiv:2508.03589](https://arxiv.org/abs/2508.03589) to be presented in **AAAI 2026**. VITA is a variational pretraining framework that learns weather representations from rich satellite data and transfers them to yield prediction tasks with limited ground-based measurements.
+This is the official pretrained model weights for the paper [arXiv:2508.03589](https://arxiv.org/abs/2508.03589). VITA is a variational pretraining framework that learns weather representations from rich satellite data and transfers them to yield prediction tasks with limited ground-based measurements.
 
 ## Overview
 
-VITA addresses the data asymmetry problem in agricultural AI: pretraining uses 31 meteorological variables from NASA POWER satellite data, while deployment relies on only 6 basic weather features. Through variational pretraining with a seasonality-aware sinusoidal prior, VITA achieves state-of-the-art performance in predicting corn and soybean yields across 763 U.S. Corn Belt counties, particularly during extreme weather years.
+VITA addresses the data asymmetry problem in agricultural AI: pretraining uses 31 meteorological variables from NASA POWER satellite data, while deployment relies on only 6 basic weather features. Through variational pretraining with a seasonality-aware sinusoidal prior, VITA achieves state-of-the-art performance in predicting corn and soybean yields across 763 U.S. Corn Belt counties, particularly during extreme weather years.
+
+## Citation
+
+```bibtex
+@inproceedings{hasan2026vita,
+      title={VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting},
+      author={Hasan, Adib and Roozbehani, Mardavij and Dahleh, Munther},
+      booktitle={Proceedings of the 40th AAAI Conference on Artificial Intelligence},
+      year={2026},
+      url={https://arxiv.org/abs/2508.03589},
+}
+```