diff --git a/CITATION.cff b/CITATION.cff
index 7963cfb..aaef280 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -1,6 +1,6 @@
 cff-version: 1.2.0
 message: "If you use FlashPDE, please cite the paper."
-title: "FlashPDE: A Drop-in Triton Operator Library for Neural PDE Solvers"
+title: "FlashPDE: A Drop-in Fused Triton Operator Library for Neural PDE Solvers"
 type: article
 authors:
   - family-names: "Zang"
@@ -12,7 +12,12 @@ authors:
   - family-names: "Cai"
     given-names: "Yongqiang"
 year: 2026
-notes: "arXiv preprint"
+identifiers:
+  - type: doi
+    value: "10.48550/arXiv.2607.18020"
+  - type: other
+    value: "arXiv:2607.18020"
+    description: "arXiv identifier"
 repository-code: "https://github.com/factnn/FlashPDE"
-url: "https://github.com/factnn/FlashPDE"
+url: "https://arxiv.org/abs/2607.18020"
 license: MIT
diff --git a/README.md b/README.md
index 321df60..23deb7e 100644
--- a/README.md
+++ b/README.md
@@ -199,15 +199,17 @@ end-to-end gains are smaller when neural-network execution dominates.
 
 ## Citation
 
-If you use FlashPDE, please cite our paper ([arXiv preprint](FlashPDE_arXiv.pdf)):
+If you use FlashPDE, please cite our paper ([arXiv:2607.18020](https://arxiv.org/abs/2607.18020)):
 
 ```bibtex
-@article{zang2026flashpde,
-  title   = {FlashPDE: A Drop-in Triton Operator Library for Neural PDE Solvers},
-  author  = {Zang, Peiyu and Xie, Bosen and Xu, Ruoxiang and Cai, Yongqiang},
-  year    = {2026},
-  note    = {arXiv preprint},
-  url     = {https://github.com/factnn/FlashPDE}
+@misc{zang2026flashpde,
+  title         = {{FlashPDE: A Drop-in Fused Triton Operator Library for Neural PDE Solvers}},
+  author        = {Peiyu Zang and Bosen Xie and Ruoxiang Xu and Yongqiang Cai},
+  year          = {2026},
+  eprint        = {2607.18020},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG},
+  url           = {https://arxiv.org/abs/2607.18020}
 }
 ```
 
diff --git a/README_zh-CN.md b/README_zh-CN.md
index d109a03..d6b4ec1 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -174,15 +174,17 @@ FlashPDE 当前面向规则笛卡尔网格和 Triton 支持的 NVIDIA GPU。增
 
 ## 引用
 
-如果使用 FlashPDE，请引用我们的论文（[arXiv 预印本](FlashPDE_arXiv.pdf)）：
+如果使用 FlashPDE，请引用我们的论文（[arXiv:2607.18020](https://arxiv.org/abs/2607.18020)）：
 
 ```bibtex
-@article{zang2026flashpde,
-  title   = {FlashPDE: A Drop-in Triton Operator Library for Neural PDE Solvers},
-  author  = {Zang, Peiyu and Xie, Bosen and Xu, Ruoxiang and Cai, Yongqiang},
-  year    = {2026},
-  note    = {arXiv preprint},
-  url     = {https://github.com/factnn/FlashPDE}
+@misc{zang2026flashpde,
+  title         = {{FlashPDE: A Drop-in Fused Triton Operator Library for Neural PDE Solvers}},
+  author        = {Peiyu Zang and Bosen Xie and Ruoxiang Xu and Yongqiang Cai},
+  year          = {2026},
+  eprint        = {2607.18020},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG},
+  url           = {https://arxiv.org/abs/2607.18020}
 }
 ```