Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 
 # MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
 
-MEG-XL is a brain-to-text foundation model pre-trained with 2.5 minutes of MEG context per sample (equivalent to 191k tokens). It is designed to capture extended neural context, enabling high data efficiency for decoding words from brain activity.
+MEG-XL is a brain-to-text foundation model pre-trained with 2.5 minutes of MEG context per sample (equivalent to 191k tokens) and published at ICML 2026. It is designed to capture extended neural context, enabling high data efficiency for decoding words from brain activity.
 
 - **Paper:** [MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training](https://huggingface.co/papers/2602.02494)
 - **Repository:** [GitHub - neural-processing-lab/MEG-XL](https://github.com/neural-processing-lab/MEG-XL)
@@ -48,10 +48,10 @@ python -m brainstorm.evaluate_criss_cross_word_classification \
 
 If you find this work helpful in your research, please cite:
 ```bibtex
-@article{jayalath2026megxl,
+@inproceedings{jayalath2026megxl,
   title={{MEG-XL}: Data-Efficient Brain-to-Text via Long-Context Pre-Training},
   author={Jayalath, Dulhan and Parker Jones, Oiwi},
-  journal={arXiv preprint arXiv:2602.02494},
+  booktitle={Forty-third International Conference on Machine Learning, {ICML} 2026, Seoul, South Korea, July 6-11, 2026},
   year={2026}
 }
 ```