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README.md

@@ -21,7 +21,7 @@ size_categories:
   <a href="https://huggingface.co/FutureMa/Eva-4B-V2"><img src="https://img.shields.io/badge/🤗-Model-yellow?style=for-the-badge" alt="Model"></a>
   <a href="https://github.com/IIIIQIIII/EvasionBench"><img src="https://img.shields.io/badge/GitHub-Repo-black?style=for-the-badge&logo=github" alt="GitHub"></a>
   <a href="https://colab.research.google.com/github/IIIIQIIII/EvasionBench/blob/main/scripts/eva4b_inference.ipynb"><img src="https://img.shields.io/badge/Colab-Quick_Start-F9AB00?style=for-the-badge&logo=googlecolab" alt="Open In Colab"></a>
-  <a href="https://arxiv.org/abs/2602.xxxxx"><img src="https://img.shields.io/badge/arXiv-Paper-red?style=for-the-badge" alt="Paper"></a>
+  <a href="https://arxiv.org/abs/2601.09142"><img src="https://img.shields.io/badge/arXiv-Paper-red?style=for-the-badge" alt="Paper"></a>
 </p>
 
 EvasionBench is a benchmark dataset for detecting **evasive answers** in earnings call Q&A sessions. The task is to classify how directly corporate management addresses questions from financial analysts.
@@ -29,7 +29,7 @@ EvasionBench is a benchmark dataset for detecting **evasive answers** in earning
 ## Dataset Description
 
 - **Repository:** [https://github.com/IIIIQIIII/EvasionBench](https://github.com/IIIIQIIII/EvasionBench)
-- **Paper:** Coming soon
+- **Paper:** [https://arxiv.org/abs/2601.09142](https://arxiv.org/abs/2601.09142)
 - **Point of Contact:** [GitHub Issues](https://github.com/IIIIQIIII/EvasionBench/issues)
 
 ### Dataset Summary
@@ -168,11 +168,14 @@ This dataset can be used to:
 If you use this dataset, please cite:
 
 ```bibtex
-@misc{evasionbench2025,
-  title={EvasionBench: A Benchmark for Detecting Evasive Answers in Earnings Calls},
-  author={EvasionBench Team},
-  year={2025},
-  url={https://github.com/IIIIQIIII/EvasionBench}
+@misc{ma2026evasionbenchlargescalebenchmarkdetecting,
+  title={EvasionBench: A Large-Scale Benchmark for Detecting Managerial Evasion in Earnings Call Q&A},
+  author={Shijian Ma and Yan Lin and Yi Yang},
+  year={2026},
+  eprint={2601.09142},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2601.09142}
 }
 ```