Update README

Author: kr11

README.md

@@ -14,14 +14,24 @@
   <a href="https://hub.docker.com/repository/docker/kangrongme/videx">
     <img src="https://img.shields.io/docker/pulls/kangrongme/videx?style=for-the-badge&logo=docker" alt="Docker Pulls"/>
   </a>
-  <img src="https://img.shields.io/badge/MySQL|Percona-8.0|_5.7-FF9800?style=for-the-badge&logo=mysql" alt="MySQL Support"/>
+  <a href="https://arxiv.org/abs/2503.23776">
+    <img src="https://img.shields.io/badge/VLDB Demo-2025-Teal?style=for-the-badge&logo=acm" alt="VLDB-Demo 2025"/>
+  </a>
+  <img src="https://img.shields.io/badge/MySQL|Percona-8.0|5.7-FF9800?style=for-the-badge&logo=mysql" alt="MySQL Support"/>
 </p>
 
-**VIDEX**: The Disaggregated, Extensible **\[VI\]**rtual in**\[DEX\]** Engine for What-If Analysis in MySQL 🚀
+**VIDEX**: The Disaggregated, Extensible **\[VI\]**rtual in**\[DEX\]** Engine for What-If Analysis in MySQL.
 - **Virtual Index**: Does not require real data, relies only on statistical information and algorithm models to accurately simulate MySQL query plans, table join orders, and index selections;
 - **Disaggregated**: VIDEX runs on a standalone instance without affecting production MySQL. Furthermore, the Statistic Server (optionally AI-enhanced to provide cardinality and ndv) can be deployed separately, enabling GPU-heterogeneous computing and seamless hot-updates.
 - **Extensible**: VIDEX offers convenient interfaces allowing users to apply models like `cardinality` and `ndv` to downstream MySQL tasks (e.g., index recommendation);
 
+## Latest News
+
+- **[2025-05-28]** 🥳🎉 VIDEX demo paper has been accepted by the **VLDB 2025 Demo Track**! 🥳🎉 "VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era" ([arXiv Preprint](https://arxiv.org/abs/2503.23776) | [How to Cite](#paper-citation))
+- **[2025-04-28]** VIDEX [v0.1.0](https://github.com/bytedance/videx/releases/tag/v0.1.0) is released.
+
+## What's VIDEX
+
 The `virtual index` (aka `hypothetical index`) aims to simulate the cost of indexes within SQL query plans, 
 thereby demonstrating to users the impact of indexes on SQL plans without the need to create actual indexes on raw instances.
 This technology is widely applied in various SQL optimization tasks, including index recommendation and table join order optimization.
@@ -55,7 +65,7 @@ We expect that VIDEX can provide users with a better platform to more easily tes
 ---
 
 
-## 1. Overview
+## 1. Architecture Overview
 
 <p align="center">
   <img src="doc/videx-structure.png" width="600">

README_zh.md

@@ -14,15 +14,26 @@
   <a href="https://hub.docker.com/repository/docker/kangrongme/videx">
     <img src="https://img.shields.io/docker/pulls/kangrongme/videx?style=for-the-badge&logo=docker" alt="Docker Pulls"/>
   </a>
-  <img src="https://img.shields.io/badge/MySQL|Percona-8.0|_5.7-FF9800?style=for-the-badge&logo=mysql" alt="MySQL Support"/>
+  <a href="https://arxiv.org/abs/2503.23776">
+    <img src="https://img.shields.io/badge/VLDB Demo-2025-Teal?style=for-the-badge&logo=acm" alt="VLDB-Demo 2025"/>
+  </a>
+  <img src="https://img.shields.io/badge/MySQL|Percona-8.0|5.7-FF9800?style=for-the-badge&logo=mysql" alt="MySQL Support"/>
 </p>
 
-**VIDEX** 为 MySQL 提供了一个解耦的、可扩展的开源虚拟索引引擎 (**\[VI\]**rtual in**\[DEX\]**)。🚀
+**VIDEX** 为 MySQL 提供了一个解耦的、可扩展的开源虚拟索引引擎 (**\[VI\]**rtual in**\[DEX\]**)。
 
 - **虚拟索引**：不需要真实数据、仅基于统计信息和算法模型，即可高精度地模拟 MySQL 产生的查询计划、模拟表连接顺序、模拟索引选择；
 - **分离式架构** (Disaggregated)：VIDEX 支持在单独的实例上部署，而不必须在原始库 MySQL 上安装；VIDEX 支持独立启动算法服务，而不必嵌入 MySQL 中；
 - **可拓展** (Extensible)：VIDEX提供了便捷的接口，用户可以将 基数估计（Cardinality）、独立值估计（NDV） 等算法模型应用于 MySQL 的下游任务中（例如索引推荐）；
 
+
+## Latest News
+
+- **[2025-05-28]** 🥳🎉VIDEX demo 论文被 **VLDB 2025 Demo Track** 接收!🥳🎉 "VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era" ([arXiv Preprint](https://arxiv.org/abs/2503.23776) | [How to Cite](#paper-citation))
+- **[2025-04-28]** VIDEX [v0.1.0](https://github.com/bytedance/videx/releases/tag/v0.1.0) 发布.
+
+## What's VIDEX
+
 “虚拟索引” 旨在模拟 SQL 查询计划中使用索引的代价（cost）， 从而向用户展示索引对 SQL 计划的影响，而无需在原始实例上创建实际索引。
 这项技术广泛应用于各种 SQL 优化任务，包括索引推荐和表连接顺序优化。
 业界许多数据库已经以官方或第三方的方式提供了虚拟索引功能，