Update README.md

README.md

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 ## 🎉 News
-- ⚡️ [2025/01/01] (update #2): We release EPlus-LLMv2, successfully addressing the challenge of auto-building energy modeling (ABEM) in complex scenarios. The new version of the platform supports a wide range of modeling scenarios encountered in real-world building applications, significantly enhancing its breadth and flexibility. Based on comprehensive datasets and a large-scale LLM, we integrate techniques such as LoRA, mixed precision training, and model quantification to reduce computational burden and achieve efficient fine-tuning (without compensating performance).
+- 📄 [2025/04/18] (update #4): The paper related to the EPlus-LLMv2 platform has been accepted for publication in _Automation in Construction_.
+[Paper here](https://doi.org/10.1016/j.autcon.2025.106223).
+- ⚡️ [2025/01/15] (update #3): We release EPlus-LLMv2, successfully addressing the challenge of auto-building energy modeling (ABEM) in complex scenarios. The new version of the platform supports a wide range of modeling scenarios encountered in real-world building applications, significantly enhancing its breadth and flexibility. Based on comprehensive datasets and a large-scale LLM, we integrate techniques such as LoRA, mixed precision training, and model quantification to reduce computational burden and achieve efficient fine-tuning (without compensating performance).
 [Paper coming soon](https://doi.org/10.1016/j.apenergy.2024.123431).
+- 📄 [2025/01/14] (update #2): Our paper on using prompt engineering to inform LLMs for automated building energy modeling has been accepted by _Energy_.
+[Paper coming soon](https://doi.org/10.1016/j.energy.2025.134548).
 - 🔥 [2024/05/016] (update #1): We first successfully implement natural language-based auto-building modeling by fine-tuning a large language model (LLM). 
 [Paper here](https://doi.org/10.1016/j.apenergy.2024.123431).