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HiDrop: Hierarchical Vision Token Reduction in MLLMs
via Late Injection, Concave Pyramid Pruning, and Early Exit

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HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit

Hao Wu*,1,2, Yingqi Fan*,1, Jinyang Dai3, Junlong Tong1,2,4, Yunpu Ma5, Xiaoyu Shen†,1,2

1Institute of Digital Twin, Eastern Institute of Technology, Ningbo

2Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative

3University of Science and Technology of China 4Shanghai Jiao Tong University

5Munich Center of Machine Learning, LMU Munich

* Equal Contribution, Corresponding Author.

Contact: haowu.ai.research@gmail.com, xyshen@eitech.edu.cn

If you find this work useful for your research and applications, please consider citing:

@misc{wu2026hidrophierarchicalvisiontoken,
      title={HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit}, 
      author={Hao Wu and Yingqi Fan and Jinyang Dai and Junlong Tong and Yunpu Ma and Xiaoyu Shen},
      year={2026},
      eprint={2602.23699},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.23699}, 
}

🔥News

  • [TODO] Code, checkpoints, and documentation are being prepared and will be released soon.
  • [2026.02.27] The preprint is now published!

💡 Highlights (TODO)

📚 Contents

  • News: Latest updates, news, and announcements.
  • Highlights: Core insights and key features highlighted in this work.
  • License: License information for this repository.
  • Acknowledgments: Credits to projects and contributors that inspired or supported this work.
  • Contact: Contact information for questions, feedback, or collaboration.
  • Related Projects: Research projects from our group (EIT-NLP) related to MLLM compression.

📄 License

This project is released under the Apache 2.0 license.

🙏 Acknowledgments

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