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README.md

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+---
+pipeline_tag: image-text-to-text
+---
+
+# V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval
+
+[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.
+
+## About V-Retrver
+
+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. 
+
+To train this agent, the authors adopted a curriculum-based learning strategy combining:
+- **Supervised Reasoning Activation:** Initial activation of retrieval-specific reasoning.
+- **Rejection-Based Refinement:** Improving reasoning reliability via rejection sampling.
+- **Reinforcement Learning:** Fine-tuning with an evidence-aligned objective.
+
+Experiments across multiple benchmarks demonstrate significant improvements in retrieval accuracy (averaging 23.0%), perception-driven reasoning reliability, and generalization.
+
+## Resources
+
+- **Paper:** [V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval](https://huggingface.co/papers/2602.06034)
+- **GitHub Repository:** [https://github.com/chendy25/V-Retrver](https://github.com/chendy25/V-Retrver)
+
+## Citation
+
+If you find this work helpful, please consider citing:
+
+```bibtex
+@article{chen2026vretrver,
+  title={V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval},
+  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},
+  journal={arXiv preprint arXiv:2602.06034},
+  year={2026}
+}
+```