Improve model card: add metadata, library name, and citation

README.md

@@ -1,12 +1,35 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - intfloat/e5-base-v2
+language:
+- en
+license: mit
+pipeline_tag: text-retrieval
+library_name: transformers
 ---
 
+# Agentic-R: Learning to Retrieve for Agentic Search
+
 ## Introduction
 
-This is the Agentic-R trained in our paper: Agentic-R: Learning to Retrieve for Agentic Search
- ([📝arXiv](https://arxiv.org/pdf/2601.11888)). Please refer our [🧩github repository](https://github.com/8421BCD/ReasonRank) for the detailed usage of our Agentic-R.
+This is the **Agentic-R** retriever model introduced in the paper [Agentic-R: Learning to Retrieve for Agentic Search](https://huggingface.co/papers/2601.11888).
+
+Agentic-R is a dense retriever specifically tailored for agentic search, where an agent interleaves multi-step reasoning with on-demand retrieval. Unlike retrievers designed for single-turn retrieval-augmented generation (RAG) that rely only on local passage utility, Agentic-R uses both local query-passage relevance and global answer correctness to measure passage utility in a multi-turn agentic search.
+
+For detailed usage instructions, training scripts, and evaluation code, please refer to the [🧩 GitHub repository](https://github.com/8421BCD/Agentic-R).
+
+## Citation
+
+If you find this work helpful, please cite our paper:
+
+```bibtex
+@misc{liu2026agenticrlearningretrieveagentic,
+      title={Agentic-R: Learning to Retrieve for Agentic Search}, 
+      author={Wenhan Liu and Xinyu Ma and Yutao Zhu and Yuchen Li and Daiting Shi and Dawei Yin and Zhicheng Dou},
+      year={2026},
+      eprint={2601.11888},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2601.11888}, 
+}
+```