Add arXiv metadata and update citation with paper links

README.md

@@ -1,7 +1,7 @@
 ---
 language:
-  - en
-  - zh
+- en
+- zh
 library_name: transformers
 license: mit
 pipeline_tag: text-generation
@@ -22,6 +22,11 @@ pipeline_tag: text-generation
     👉 One click to <a href="https://chat.z.ai">GLM-5</a>.
 </p>
 
+<p align="center">
+    [<a href="https://huggingface.co/papers/2602.15763" target="_blank">Paper</a>] 
+    [<a href="https://github.com/zai-org/GLM-5" target="_blank">GitHub</a>]
+</p>
+
 ## Introduction
 
 We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
@@ -149,4 +154,12 @@ vLLM, SGLang, KTransformers, and xLLM all support local deployment of GLM-5. A s
 
 ## Citation
 
-Our technical report is coming soon.
+```bibtex
+@article{glm5team2026glm5,
+  title={GLM-5: from Vibe Coding to Agentic Engineering},
+  author={GLM-5 Team and Aohan Zeng and Xin Lv and Zhenyu Hou and Zhengxiao Du and Qinkai Zheng and Bin Chen and Da Yin and Chendi Ge and Chengxing Xie and others},
+  journal={arXiv preprint arXiv:2602.15763},
+  year={2026},
+  url={https://huggingface.co/papers/2602.15763}
+}
+```