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--- |
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pipeline_tag: image-text-to-text |
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--- |
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# V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval |
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[V-Retrver](https://huggingface.co/papers/2602.06034) is an evidence-driven retrieval framework that reformulates multimodal retrieval as an agentic reasoning process grounded in visual inspection. |
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## About V-Retrver |
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V-Retrver enables a Multimodal Large Language Model (MLLM) to selectively acquire visual evidence during reasoning via external visual tools. It performs a **multimodal interleaved reasoning** process that alternates between hypothesis generation and targeted visual verification. |
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To train this agent, the authors adopted a curriculum-based learning strategy combining: |
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- **Supervised Reasoning Activation:** Initial activation of retrieval-specific reasoning. |
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- **Rejection-Based Refinement:** Improving reasoning reliability via rejection sampling. |
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- **Reinforcement Learning:** Fine-tuning with an evidence-aligned objective. |
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Experiments across multiple benchmarks demonstrate significant improvements in retrieval accuracy (averaging 23.0%), perception-driven reasoning reliability, and generalization. |
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## Resources |
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- **Paper:** [V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval](https://huggingface.co/papers/2602.06034) |
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- **GitHub Repository:** [https://github.com/chendy25/V-Retrver](https://github.com/chendy25/V-Retrver) |
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## Citation |
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If you find this work helpful, please consider citing: |
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```bibtex |
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@article{chen2026vretrver, |
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title={V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval}, |
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author={Chen, Dongyang and Wang, Chaoyang and Su, Dezhao and Xiao, Xi and Zhang, Zeyu and Xiong, Jing and Li, Qing and Shang, Yuzhang and Ka, Shichao}, |
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journal={arXiv preprint arXiv:2602.06034}, |
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year={2026} |
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} |
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``` |
