update README

README.md

@@ -50,11 +50,12 @@ configs:
 <p align="center">
 <a href="https://huggingface.co/collections/openbmb/ultradata">📦 UltraData Collection</a> |
 <a href="https://ultradata.openbmb.cn/">🌐 UltraData</a> |
+📖 Tech Report (Coming Soon) |
 <a href="https://huggingface.co/datasets/openbmb/UltraData-Code">🤗 UltraData-Code</a> |
 <a href="https://huggingface.co/openbmb/UltraData-Code-L2-Classifier">🤗 UltraData-Code-L2 Classifier</a>
 </p>
 
-<p align="center">English | <a href="README_ZH.md">中文</a></p>
+<p align="center">English | <a href="https://huggingface.co/datasets/openbmb/UltraData-Stack-v3-L2/blob/main/README_ZH.md">中文</a></p>
 
 ## 📚 Introduction
 
@@ -62,6 +63,13 @@ configs:
 
 Construction has two stages. First, source records are cleaned by removing URL-only entries, overly short records, and records containing garbled or malformed text. Second, a language-specific selector combines file-role prediction, algorithmic-relevance scoring, an `EXCLUDE` veto, and a shared code-quality constraint. The relevance threshold is calibrated separately for each language. The resulting dataset retains **20.10%** of the cleaned 11-language candidate pool, and is intended to concentrate interpretable computation and problem-solving logic while preserving broad language coverage.
 
+## 📢 What's New
+
+- **[2026.10.12]** The [***UltraData-Stack-v3-L2***](https://huggingface.co/datasets/openbmb/UltraData-Stack-v3-L2) dataset is released! 🚀🚀🚀
+- **[2026.09.07]** The [***UltraData-Code***](https://huggingface.co/datasets/openbmb/UltraData-Code) dataset is released! It is a complete implementation of the [UltraData](https://ultradata.openbmb.cn/) [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003). It covers four code data states from L0 through L3, with each level corresponding to a distinct construction stage. This release currently open-sources **L2** (\~400B tokens) and **L3** (\~150B tokens) across 11 programming languages. 🚀🚀🚀
+- **[2026.09.07]** ***[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) is released!***, the second model in the MiniCPM5 series after MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios. It reaches **2B-class open-source SOTA**, remains competitive with 4B-class models, and shows particular advantages in coding, mathematics, long-context understanding, tool use, and agentic tasks. UltraData-RL-2609 serves as the core RL dataset for MiniCPM5-2B. 🚀🚀🚀
+- **[2026.02.08]** The [***UltraData***](https://ultradata.openbmb.cn/) platform is now live, introducing the [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003).  🔍🔍🔍
+
 ## 💡 Highlights
 
 - **Transferable algorithmic-relevance selection:** The same selection objective and model interfaces used for UltraData-Code-L2 are adapted to Stack-v3-train, with language-specific evidence and thresholds.

README_ZH.md

@@ -7,11 +7,12 @@
 <p align="center">
 <a href="https://huggingface.co/collections/openbmb/ultradata">📦 UltraData 合集</a> |
 <a href="https://ultradata.openbmb.cn/">🌐 UltraData</a> |
+📖 技术报告（即将发布）|
 <a href="https://huggingface.co/datasets/openbmb/UltraData-Code">🤗 UltraData-Code</a> | 
 <a href="https://huggingface.co/openbmb/UltraData-Code-L2-Classifier">🤗 UltraData-Code-L2 分类器</a>
 </p>
 
-<p align="center"><a href="README.md">English</a> | 中文</p>
+<p align="center"><a href="https://huggingface.co/datasets/openbmb/UltraData-Stack-v3-L2/blob/main/README.md">English</a> | 中文</p>
 
 ## 📚 简介
 
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 数据构建分为两个阶段。首先，基础清洗会移除全文仅包含 URL、长度过短以及含乱码或格式异常的记录。随后，针对每种语言分别运行 L2 选择器，综合文件角色预测、算法相关性评分、`EXCLUDE` 否决规则与统一的代码质量约束，并为每种语言单独校准算法相关性阈值。最终数据集保留清洗后 11 语言候选池的约 **20.10%**，旨在提高具有可解释计算过程和问题求解逻辑的代码密度，同时保持广泛的语言覆盖。
 
+## 📢 最新动态
+
+- **[2026.10.12]** [***UltraData-Stack-v3-L2***](https://huggingface.co/datasets/openbmb/UltraData-Stack-v3-L2) 数据集正式发布！🚀🚀🚀
+- **[2026.09.07]** [***UltraData-Code***](https://huggingface.co/datasets/openbmb/UltraData-Code) 数据集正式发布！这是 [UltraData](https://ultradata.openbmb.cn/) [L0-L4 分级治理体系](https://arxiv.org/pdf/2602.09003) 的完整实现，覆盖 L0 至 L3 四个代码数据状态，各层对应不同的数据构建阶段。本次开源 **L2**（约 400B tokens）和 **L3**（约 150B tokens），覆盖 11 种编程语言。🚀🚀🚀
+- **[2026.09.07]** ***[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) 正式发布！*** 作为 MiniCPM5 系列在 MiniCPM5-1B 之后的第二款模型，这是一款沿用同一套训练配方放大的 2B 稠密 Transformer，面向端侧、本地部署与资源受限场景，达到 **2B 级开源 SOTA**，整体可与 4B 级模型竞争，并在代码、数学、长上下文、工具调用与 Agent 任务上相对同规模模型更具优势。UltraData-RL-2609 是 MiniCPM5-2B 的核心 RL 数据。🚀🚀🚀
+- **[2026.02.08]** [***UltraData***](https://ultradata.openbmb.cn/) 平台正式上线，并发布 [L0-L4 分级数据管理框架](https://arxiv.org/pdf/2602.09003)。🔍🔍🔍
+
 ## 💡 亮点
 
 - **可迁移的算法相关性精筛：** 将 UltraData-Code-L2 使用的统一选择目标和模型接口迁移至 Stack-v3-train，并针对不同语言适配识别证据与筛选阈值。