Update README.md

README.md

@@ -96,8 +96,8 @@ The molecular toxicity repair task requires models to:
 ### Task Overview
 | Dataset | Task Type | # Molecules | Description |
 |---------|-----------|-------------|-------------|
-| **Ames** | Binary Classification | 50 | Mutagenicity testing |
-| **Carcinogens_Lagunin** | Binary Classification | 50 | Carcinogenicity prediction |
+| **AMES** | Binary Classification | 50 | Mutagenicity testing |
+| **Carcinogens** | Binary Classification | 50 | Carcinogenicity prediction |
 | **ClinTox** | Binary Classification | 50 | Clinical toxicity data |
 | **DILI** | Binary Classification | 50 | Drug-induced liver injury |
 | **hERG** | Binary Classification | 50 | hERG channel inhibition |
@@ -165,12 +165,14 @@ Our systematic evaluation of ~30 mainstream MLLMs reveals:
 If you use this dataset in your research, please cite:
 
 ```bibtex
-@article{lin2025breaking,
-  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},
-  journal={arXiv preprint},
-  year={2025},
-  note={Paper submitted to arXiv}
+@misc{lin2025breakingbadmoleculesmllms,
+      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},
+      eprint={2506.10912},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2506.10912}, 
 }
 ```