Add the CAE-FastWAM citation.

ASSET_SHA256SUMS

@@ -10,5 +10,5 @@ d2023cc6a5a33abd405c3a9f63b7fabc84df6c917798bd7080114bcbe9294d89  stats/libero_s
 905ccc23cf99504e6f17f9fac2b5115896304be2d2dc0a9522f8886095c0548c  videos/libero-object.mp4
 b5393c1ae185b1d05ad79edde9b5dc7b10543694b5c6be0d44084362e962e51d  videos/libero-goal.mp4
 38e0b77c8ec5988f3357ad0e28b5456b42c95b5698e96961d5d0cb2f9433751a  videos/libero-long.mp4
-d355ee8a5c574167979055e2540dfb9d86734a60a67637a29eb5b01b17a2a742  README.md
+f0ae5036e9a6952a06c2dc7c91fe6fd4a791a79ec1fdc2ae0043da24c424c418  README.md
 f8337593ad4c0174982bcd6219ba666128bf5b2bf587c338921b89e2dfd7d1ff  replay.mp4

README.md

@@ -69,7 +69,17 @@ They are a modified version of [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wa
 
 Training data: [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) (CC BY 4.0), in the LeRobot format published as [yuanty/LIBERO-fastwam](https://huggingface.co/datasets/yuanty/LIBERO-fastwam) (CC BY 4.0). This is the dataset used by the FastWAM LIBERO recipe, and it is used as published. No changes were made to the training data. No extra training data was used beyond the FastWAM recipe.
 
-## Citation
+## Citing
+
+```bibtex
+@misc{CAE-FastWAM,
+    title = {CAE-FastWAM: Compact Action Expert for FastWAM},
+    url = {https://huggingface.co/amd/CAE-FastWAM},
+    author = {Hitesh Kandala, Raghav Goyal, Pratik Prabhanjan Brahma, Emad Barsoum},
+    month = {October},
+    year = {2026}
+}
+```
 
 - T. Yuan, Z. Dong, Y. Liu, and H. Zhao, "Fast-WAM: Do World Action Models Need Test-time Future Imagination?" arXiv:2603.16666, 2026.
 - B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone, "LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning," NeurIPS 2023.