docs: update README

Author: ChenZiHong-Gavin

README.md

@@ -51,14 +51,14 @@ Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture co
 After data generation, you can use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) and [xtuner](https://github.com/InternLM/xtuner) to finetune your LLMs.
 
 ## 📌 Latest Updates
-- 🎉 **2026.04.13**: The paper based on GraphGen, *Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains*, has been accepted to the **ACL 2026** Main Conference! Congratulations!
+- 🎉 **2026.04.13**: The paper based on GraphGen, *Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains*, has been accepted to the **ACL 2026** Main Conference! Congratulations! [Code](https://github.com/superfarther/K2V)
 - **2026.02.04**: We support HuggingFace Datasets as input data source for data generation now.
 - **2026.01.15**: **LLM benchmark synthesis** now supports single/multiple-choice & fill-in-the-blank & true-or-false—ideal for education 🌟🌟
-- **2025.12.26**: Knowledge graph evaluation metrics about accuracy (entity/relation), consistency (conflict detection), structural robustness (noise, connectivity, degree distribution)
 
 <details>
 <summary>History</summary>
 
+- **2025.12.26**: Knowledge graph evaluation metrics about accuracy (entity/relation), consistency (conflict detection), structural robustness (noise, connectivity, degree distribution)
 - **2025.12.16**: Added [rocksdb](https://github.com/facebook/rocksdb) for key-value storage backend and [kuzudb](https://github.com/kuzudb/kuzu) for graph database backend support.
 - **2025.12.16**: Added [vllm](https://github.com/vllm-project/vllm) for local inference backend support.
 - **2025.12.16**: Refactored the data generation pipeline using [ray](https://github.com/ray-project/ray) to improve the efficiency of distributed execution and resource management.

README_zh.md

@@ -53,15 +53,15 @@ GraphGen 首先根据源文本构建细粒度的知识图谱，然后利用期
 在数据生成后，您可以使用[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 和 [xtuner](https://github.com/InternLM/xtuner)对大语言模型进行微调。
 
 ## 📌 最新功能
-- 🎉 **2026.04.13**：基于 GraphGen 的论文 *Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains* 已被 **ACL 2026** 主会录取！恭喜！
+- 🎉 **2026.04.13**：基于 GraphGen 的论文 *Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains* 已被 **ACL 2026** 主会录取！恭喜！[代码](https://github.com/superfarther/K2V)
 - **2026.02.04**：支持使用直接读入 HuggingFace 数据集进行数据生成
 - **2026.01.15**：合成垂域评测数据（单选题、多选题、填空题和判断题型）🌟🌟
-- **2025.12.26**：引入知识图谱评估指标，包括准确度评估（实体/关系抽取质量）、一致性评估（冲突检测）和结构鲁棒性评估（噪声比、连通性、度分布）
 
 
 <details>
 <summary>历史更新记录</summary>
 
+- **2025.12.26**：引入知识图谱评估指标，包括准确度评估（实体/关系抽取质量）、一致性评估（冲突检测）和结构鲁棒性评估（噪声比、连通性、度分布）
 - **2025.12.16**：支持 [rocksdb](https://github.com/facebook/rocksdb) 作为键值存储后端, [kuzudb](https://github.com/kuzudb/kuzu) 作为图数据库后端。
 - **2025.12.16**：支持 [vllm](https://github.com/vllm-project/vllm) 作为本地推理后端。
 - **2025.12.16**：使用 [ray](https://github.com/ray-project/ray) 重构了数据生成 pipeline，提升了分布式执行和资源管理的效率。