| 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** 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}, | |
| } | |
| ``` |