Update README.md

README.md

@@ -67,6 +67,10 @@ dataset_info:
 [![arXiv](https://img.shields.io/badge/arXiv-paper-red.svg)](https://arxiv.org/pdf/2506.10912)
 [![Dataset](https://img.shields.io/badge/🤗%20Hugging%20Face-ToxiMol-blue)](https://huggingface.co/datasets/DeepYoke/ToxiMol-benchmark)
 
+## 🔥 News
+
+- 🏆 **ToxiMol** has been accepted as an **Oral** presentation at the **32nd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), AI for Sciences Track**.
+
 ## Overview
 
 **ToxiMol** is the first comprehensive benchmark for **molecular toxicity repair** tailored to general-purpose **Multimodal Large Language Models (MLLMs)**. This is the dataset repository for the paper "Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?".
@@ -165,10 +169,10 @@ Our systematic evaluation of ~30 mainstream MLLMs reveals:
 If you use this dataset in your research, please cite:
 
 ```bibtex
-@misc{lin2025breakingbadmoleculesmllms,
+@misc{lin2026breakingbadmoleculesmllms,
       title={Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?}, 
-      author={Fei Lin and Ziyang Gong and Cong Wang and Yonglin Tian and Tengchao Zhang and Xue Yang and Gen Luo and Fei-Yue Wang},
-      year={2025},
+      author={Fei Lin and Ziyang Gong and Cong Wang and Tengchao Zhang and Yonglin Tian and Yining Jiang and Ji Dai and Chao Guo and Xiaotong Yu and Xue Yang and Gen Luo and Fei-Yue Wang},
+      year={2026},
       eprint={2506.10912},
       archivePrefix={arXiv},
       primaryClass={cs.AI},