Add pipeline tag, library metadata and research links
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by nielsr HF Staff - opened
README.md
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datasets:
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- GenSearcher/Train-Data
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base_model:
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- Qwen/Qwen3-VL-8B-Instruct
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# Gen-Searcher-8B Model
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For inference, please refer to:
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Code: https://github.com/tulerfeng/Gen-Searcher
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# 👀 Intro
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<div align="center">
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<img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/teaser.jpg?raw=true" alt="
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</div>
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We introduce **Gen-Searcher**, as the first attempt to train a multimodal **deep research agent** for image generation that requires complex real-world knowledge. Gen-Searcher can **search the web, browse evidence, reason over multiple sources, and search visual references** before generation, enabling more accurate and up-to-date image synthesis in real-world scenarios.
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We build two dedicated training datasets **Gen-Searcher-SFT-10k**, **Gen-Searcher-RL-6k** and one new benchmark **KnowGen** for search-grounded image generation.
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All code, models, data, and benchmark are fully released.
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## 🎥 Demo
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#### Inference Process Example
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<div align="center">
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<img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/example.jpg?raw=true" alt="
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</div>
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For more examples, please refer to our website [[🌐Project Page]](https://gen-searcher.vercel.app/)
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---
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base_model:
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- Qwen/Qwen3-VL-8B-Instruct
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datasets:
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- GenSearcher/Train-Data
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library_name: transformers
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pipeline_tag: image-text-to-text
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license: apache-2.0
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# Gen-Searcher-8B Model
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This repository contains the Gen-Searcher-8B model presented in [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://arxiv.org/abs/2603.28767).
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[**Project Page**](https://gen-searcher.vercel.app/) | [**GitHub Repository**](https://github.com/tulerfeng/Gen-Searcher) | [**Paper**](https://arxiv.org/abs/2603.28767)
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# 👀 Intro
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<div align="center">
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<img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/teaser.jpg?raw=true" alt="Gen-Searcher Teaser" width="80%">
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</div>
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We introduce **Gen-Searcher**, as the first attempt to train a multimodal **deep research agent** for image generation that requires complex real-world knowledge. Gen-Searcher can **search the web, browse evidence, reason over multiple sources, and search visual references** before generation, enabling more accurate and up-to-date image synthesis in real-world scenarios.
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We build two dedicated training datasets **Gen-Searcher-SFT-10k**, **Gen-Searcher-RL-6k** and one new benchmark **KnowGen** for search-grounded image generation.
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All code, models, data, and benchmark are fully released.
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## 🎥 Demo
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#### Inference Process Example
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<div align="center">
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<img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/example.jpg?raw=true" alt="Inference Process Example" width="85%">
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</div>
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For more examples, please refer to our website [[🌐Project Page]](https://gen-searcher.vercel.app/)
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## 🚀 Training and Inference
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For detailed instructions on setup, SFT/RL training, and inference, please refer to the [official GitHub repository](https://github.com/tulerfeng/Gen-Searcher).
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## 📐 Citation
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If you find our work helpful for your research, please consider citing our work:
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```bibtex
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@article{feng2025gensearcher,
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title={Gen-Searcher: Reinforcing Agentic Search for Image Generation},
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author={Feng, Kaituo and Zhang, Manyuan and Chen, Shuang and Lin, Yunlong and Fan, Kaixuan and Jiang, Yilei and Li, Hongyu and Zheng, Dian and Wang, Chenyang and Yue, Xiangyu},
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journal={arXiv preprint arXiv:2603.28767},
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year={2025}
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}
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```
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