diff --git a/README.md b/README.md
index a1474fd..b9ddce9 100644
--- a/README.md
+++ b/README.md
@@ -30,9 +30,12 @@
 - OpenDAIR-V2X: An open-sourced framework for supporting the research on  vehicle-to-everything autonomous driving.
 
 ## News <a name="news"></a>
-* [2024.09] 🔥 Our [V2X-Gaph](https://github.com/AIR-THU/V2X-Graph) has been accepted by NeurIPs2024! V2X-Graph. V2X-Graph is the first interpretable and end-to-end learning framework for cooperative motion forecasting.
-* [2024.07] 🔥 Our CTCE[https://arxiv.org/abs/2408.10531] has been accpeted by ITSC2024! CTCE is a novel sequential framework for cooperative perception. 
-* [2024.04] Our [UniV2X](https://arxiv.org/abs/2404.00717) available on arXiv. UniV2X is the first end-to-end framework that unifies all vital modules as well as diverse driving views into a network for cooperative autonomous driving. Code will be [here](https://github.com/AIR-THU/UniV2X).
+* [2025.01] 🔥 We will hold 2nd MEIS Workshop at CVPR2025. More information will be [MEIS Workshop](https://coop-intelligence.github.io/). We will call for paper and hold three challenges.
+* [2025.01] 🔥 We have released v1.0 code for UniV2X at [here](https://github.com/AIR-THU/UniV2X).
+* [2024.12] Our [UniV2X](https://arxiv.org/abs/2404.00717) has been accepted by AAAI2025. UniV2X is the first end-to-end framework that unifies all vital modules as well as diverse driving views into a network for cooperative autonomous driving.
+* [2024.10] We held 1st cooperative intelligence workshop at ECCV2024. More information is [MAAS Workshop](https://coop-intelligence.github.io/eccv2024/).
+* [2024.09] Our [V2X-Gaph](https://github.com/AIR-THU/V2X-Graph) has been accepted by NeurIPs2024! V2X-Graph. V2X-Graph is the first interpretable and end-to-end learning framework for cooperative motion forecasting.
+* [2024.07] Our CTCE[https://arxiv.org/abs/2408.10531] has been accpeted by ITSC2024! CTCE is a novel sequential framework for cooperative perception. 
 * [2024.03] Our new Dataset RCooper, a real-world large-scale dataset for roadside cooperative perception, has been accepted by CVPR2024! Please follow [RCooper](https://github.com/AIR-THU/DAIR-RCooper) for the latest news.
 * [2024.01] Our [QUEST](https://arxiv.org/abs/2308.01804) has been been accpeted by ICRA2024.
 * [2023.10] We have released the code for [V2X-Seq-SPD](https://github.com/AIR-THU/DAIR-V2X) and [V2X-Seq-TFD](https://github.com/AIR-THU/DAIR-V2X-Seq).