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---
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},
}
``` |