| title | ReToken: One Token to Improve Vision-Language Models for Visual Retrieval |
|---|---|
| authors | Yao Xiao, Reuben Tan, Zhen Zhu, Yuqun Wu, Jianfeng Gao, Derek Hoiem |
| date | 2026-07-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.28627 |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at once is computationally infeasible under GPU memory constraints. We present ReToken, a single learnable embedding trained as an explicit retrieval target that selects a sparse set of query-relevant visual tokens from a pre-filled visual KV cache. Trained on only a small image-QA dataset, ReToken yields consistent gains across image and video benchmarks: on Visual Haystacks it improves Qwen3VL-8B by 13.4 points and InternVL3.5 by 12.4 points (>20% relative), and on LVBench it transfers zero-shot to long video for an 8.0-point gain with Qwen3VL-8B. Thanks to its lightweight design, both training and long-video inference fit on a single H100. Code is available at: https://github.com/avaxiao/ReToken |
cite
@misc{xiao2026retoken,
title={ReToken: One Token to Improve Vision-Language Models for Visual Retrieval},
author={Yao Xiao and Reuben Tan and Zhen Zhu and Yuqun Wu and Jianfeng Gao and Derek Hoiem},
year={2026},
eprint={2607.28627},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.28627}
}