Add arXiv badge and citation

README.md

@@ -8,6 +8,7 @@ task_categories:
 # Generative Embedding Benchmark
 
 <div align="center">
+  <a href="https://arxiv.org/abs/2608.06972"><img src="https://img.shields.io/badge/arXiv-2608.06972-b31b1b.svg" alt="arXiv"></a>
   <a href="https://github.com/LimitedMouse/Generative-Embedding-Benchmark"><img src="https://img.shields.io/badge/GitHub-Code-lightgrey" alt="GitHub Code"></a>
   <a href="https://huggingface.co/LimitedMouse/Generative-Embedding-Benchmark-Checkpoints"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoints-yellow" alt="Hugging Face Checkpoints"></a>
 </div>
@@ -39,5 +40,19 @@ access requirements.
 - `summary.json`: sampling quotas and aggregate attrition statistics.
 - `leakage_audit.json`: development/test image-leakage audit.
 
+## Citation
+
+```bibtex
+@misc{li2026generativeembeddingbenchmark,
+  title={Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding?},
+  author={Yun Li and Biao Yang and Peixi Wu and Yunhao Zhou and Mingzhou Jiang and Wei Yuan and Fan Yang and Wenwu Ou},
+  year={2026},
+  eprint={2608.06972},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2608.06972},
+}
+```
+
 The Apache-2.0 license applies only to GEB-authored manifests and metadata. It
 does not alter the terms of the upstream datasets.