[feat] update readme

README.md

@@ -126,7 +126,7 @@
 ### 👉**更新日志**
 
 <details close> 
-<summary> <b>2025-10-24 (newest🎉)</b> </summary>
+<summary> <b>2025-10-24</b> </summary>
 
 - 🔥 新增RLAIF训练算法：PPO、GRPO、SPO（从0原生实现）
 - 新增断点续训功能：支持训练自动恢复、跨GPU数量恢复、wandb记录连续性
@@ -179,50 +179,28 @@ MiniMind2系列旧模型均经过权重映射+（微调训练）QKVO线性层校
 
 </details>
 
+<details close>
+<summary> <b>More...</b> </summary>
 
-
+**2024-10-05**
-<details close> 
-<summary> <b>2024-10-05</b> </summary>
-
 - 为MiniMind拓展了多模态能力之---视觉
 - 移步孪生项目[minimind-v](https://github.com/jingyaogong/minimind-v)查看详情！
 
-</details>
+**2024-09-27**
-
-
-
-<details close> 
-<summary> <b>2024-09-27</b> </summary>
-
 - 09-27更新pretrain数据集的预处理方式，为了保证文本完整性，放弃预处理成.bin训练的形式（轻微牺牲训练速度）。
 - 目前pretrain预处理后的文件命名为：pretrain_data.csv。
 - 删除了一些冗余的代码。
 
-</details>
+**2024-09-17**
-
-
-<details close> 
-<summary> <b>2024-09-17</b> </summary>
-
 - 更新minimind-v1-moe模型
 - 为了防止歧义，不再使用mistral_tokenizer分词，全部采用自定义的minimind_tokenizer作为分词器。
 
-</details>
+**2024-09-01**
-
-
-<details close>
-<summary> <b>2024-09-01</b> </summary>
-
 - 更新minimind-v1 (108M)模型，采用minimind_tokenizer，预训练轮次3 + SFT轮次10，更充分训练，性能更强。
 - 项目已部署至ModelScope创空间，可以在此网站上体验：
 - [🔗ModelScope在线体验🔗](https://www.modelscope.cn/studios/gongjy/minimind)
 
-</details>
+**2024-08-27**
-
-
-<details close> 
-<summary> <b>2024-08-27</b> </summary>
-
 - 项目首次开源
 
 </details>
@@ -1897,6 +1875,38 @@ ollama run jingyaogong/minimind2 # 其他可选 minimind2-r1 / minimind2-small /
   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=jingyaogong/minimind&type=Date"/>
 </picture>
 
+## 🎉 Awesome Work using MiniMind
+
+本模型抛砖引玉地促成了一些可喜成果的落地，感谢研究者们的认可：
+
+- ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis [[arxiv](https://arxiv.org/pdf/2502.17475)]
+
+- Binary-Integer-Programming Based Algorithm for Expert Load Balancing in Mixture-of-Experts Models [[arxiv](https://arxiv.org/pdf/2502.15451)]
+
+- LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text [[arxiv](https://arxiv.org/pdf/2505.24826)]
+
+- On the Generalization Ability of Next-Token-Prediction Pretraining [[ICML 2025](https://openreview.net/forum?id=hLGJ1qZPdu)]
+
+- Building Large Models from Scratch: From Neural Networks to Transformer by Wang Shuang, Mou Chen, Wang Haoyi - Tsinghua University Press
+
+- FedBRB: A Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning [[TMC 2025](https://ieeexplore.ieee.org/abstract/document/11168259)]
+
+- 进行中...
+
+
+# 🎓 Citation
+
+If you find MiniMind helpful in your research or work, please cite:
+
+```bibtex
+@misc{minimind,
+  title={MiniMind: Train a Tiny LLM from scratch},
+  author={Jingyao Gong},
+  year={2024},
+  howpublished={\url{https://github.com/jingyaogong/minimind}}
+}
+```
+
 # License
 
 This repository is licensed under the [Apache-2.0 License](LICENSE).

README_en.md

@@ -131,7 +131,7 @@ We hope this open-source project can help LLM beginners get started quickly!
 ### 👉**Update Log**
 
 <details close> 
-<summary> <b>2025-10-24 (newest🎉)</b> </summary>
+<summary> <b>2025-10-24</b> </summary>
 
 - 🔥 Added RLAIF training algorithms: PPO, GRPO, SPO (native implementation from scratch)
 - Added checkpoint resume training: supports automatic training recovery, cross-GPU recovery, wandb continuity
@@ -184,43 +184,28 @@ After this update, maintenance of the entire minimind-v1 series will be abandone
 
 </details>
 
-<details close> 
+<details close>
-<summary> <b>2024-10-05</b> </summary>
+<summary> <b>More...</b> </summary>
 
+**2024-10-05**
 - Extended MiniMind with multimodal capabilities---Vision
 - Check out the twin project [minimind-v](https://github.com/jingyaogong/minimind-v) for details!
 
-</details>
+**2024-09-27**
-
-<details close> 
-<summary> <b>2024-09-27</b> </summary>
-
 - 09-27 updated the preprocessing method for the pretrain dataset, abandoned preprocessing into .bin format for training to ensure text integrity (slightly sacrificing training speed).
 - Current pretrain preprocessing file is named: pretrain_data.csv.
 - Removed some redundant code.
 
-</details>
+**2024-09-17**
-
-<details close> 
-<summary> <b>2024-09-17</b> </summary>
-
 - Updated minimind-v1-moe model
 - To avoid ambiguity, no longer using mistral_tokenizer for tokenization, completely using custom minimind_tokenizer as the tokenizer.
 
-</details>
+**2024-09-01**
-
-<details close>
-<summary> <b>2024-09-01</b> </summary>
-
 - Updated minimind-v1 (108M) model, using minimind_tokenizer, 3 pretraining rounds + 10 SFT rounds, more thorough training, stronger performance.
 - Project has been deployed to ModelScope creation space, you can experience it on this website:
 - [🔗ModelScope Online Experience🔗](https://www.modelscope.cn/studios/gongjy/minimind)
 
-</details>
+**2024-08-27**
-
-<details close> 
-<summary> <b>2024-08-27</b> </summary>
-
 - Project first open-sourced
 
 </details>
@@ -1818,6 +1803,38 @@ I am a language model...
   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=jingyaogong/minimind&type=Date"/>
 </picture>
 
+## 🎉 Awesome Work using MiniMind
+
+This model has inspired some exciting research outcomes. Thank you to all researchers for your recognition:
+
+- ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis [[arxiv](https://arxiv.org/pdf/2502.17475)]
+
+- Binary-Integer-Programming Based Algorithm for Expert Load Balancing in Mixture-of-Experts Models [[arxiv](https://arxiv.org/pdf/2502.15451)]
+
+- LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text [[arxiv](https://arxiv.org/pdf/2505.24826)]
+
+- On the Generalization Ability of Next-Token-Prediction Pretraining [[ICML 2025](https://openreview.net/forum?id=hLGJ1qZPdu)]
+
+- Building Large Models from Scratch: From Neural Networks to Transformer by Wang Shuang, Mou Chen, Wang Haoyi - Tsinghua University Press
+
+- FedBRB: A Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning [[TMC 2025](https://ieeexplore.ieee.org/abstract/document/11168259)]
+
+- Continuously...
+
+
+# 🎓 Citation
+
+If you find MiniMind helpful in your research or work, please cite:
+
+```bibtex
+@misc{minimind,
+  title={MiniMind: Train a Tiny LLM from scratch},
+  author={Jingyao Gong},
+  year={2024},
+  howpublished={\url{https://github.com/jingyaogong/minimind}}
+}
+```
+
 # License
 
 This repository is licensed under the [Apache-2.0 License](LICENSE).