Update README.md

README.md

@@ -38,7 +38,7 @@ tags:
 These are the official pre-trained model weights and configuration files for **D<sup>2</sup>MoRA**, a novel **diversity-regulated asymmetric MoE-LoRA decomposition framework** for **parameter-efficient fine-tuning (PEFT)** of large language models in **multi-task adaptation** scenarios.
 
 🔗 **Paper:** [Accepted by AAAI 2026]  
-🔗 **GitHub Repository:** [softwavec/D2MoRA](https://github.com/softwavec/D2MoRA)
+🔗 **GitHub Repository:** [softwavec/D2MoRA](https://github.com/iLearn-Lab/AAAI26-D2MoRA)
 
 ---
 
@@ -96,7 +96,7 @@ These weights are designed to be used directly with the official **D<sup>2</sup>
 Clone the GitHub repository and install dependencies following the official repository instructions:
 
 ```bash
-git clone https://github.com/softwavec/D2MoRA.git
+git clone https://github.com/iLearn-Lab/AAAI26-D2MoRA.git
 cd D2MoRA
 ```
 
@@ -141,10 +141,13 @@ Please use the official repository scripts for training and evaluation.
 If you find our work or these model weights useful in your research, please consider leaving a **Star** ⭐️ on our GitHub repo and citing our paper:
 
 ```bibtex
-@inproceedings{INTENT,
-  title={INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval},
-  author={Chen, Zhiwei and Hu, Yupeng and Fu, Zhiheng and Li, Zixu and Huang, Jiale and Huang, Qinlei and Wei, Yinwei},
+@inproceedings{zuo2026d2mora,
+  title={D2MoRA: Diversity-Regulated Asymmetric MoE-LoRA Decomposition for Efficient Multi-Task Adaptation},
+  author={Zuo, Jianhui and Song, Xuemeng and Wen, Haokun and Liu, Meng and Hu, Yupeng and Wang, Jiuru and Nie, Liqiang},
   booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={40},
+  number={34},
+  pages={29286--29294},
   year={2026}
 }
 ```