| title | Xiaomi-Lens-0 Technical Report |
|---|---|
| authors | Pei Fu, Yike Liu, Yue Sun, Hanmo Wang, Xiuwen Xi, Ruoceng Zhang, Shaojie Zhang, Chao Jiang, Hang Li, Bingrui Liu, Zhenbo Luo, Heng Qu, Changjian Xu, Ming Xu, Jian Luan |
| date | 2026-10-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.23279 |
| affiliation | SeerRay-Lab |
| upvotes | 60 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Multimodal large language models (MLLMs) answer questions about whole images well, yet they still struggle with fine-grained region understanding. A key reason is that text-only supervision never specifies which regions support an answer. To close this gap, we present Xiaomi-Lens-0, a 4B MLLM built on Qwen3.5 that predicts masks from its own visual representations. Specifically, a lightweight decoder combines vision-encoder patch features with visual latents, the language model's mask-token states, so mask supervision reaches the shared backbone without an additional segmentation encoder. Because each latent decodes to a mask, the model can interleave reasoning with region-specific latents and thus think with visual latents, while grounded reasoning traces supervise the regions it visits during chain-of-thought. As a result, across all eight RefCOCO, RefCOCO+, and RefCOCOg splits, Xiaomi-Lens-0 gains 5.6 points on average over Qwen3.5 in referring comprehension and achieves the highest segmentation cIoU among compared models. Moreover, it surpasses Qwen3.5 across all perception benchmarks. Homepage: https://seerray-lab.github.io/Xiaomi-Lens-0{this https URL} |
cite
@misc{fu2026xiaomi,
title={Xiaomi-Lens-0 Technical Report},
author={Pei Fu and Yike Liu and Yue Sun and Hanmo Wang and Xiuwen Xi and Ruoceng Zhang and Shaojie Zhang and Chao Jiang and Hang Li and Bingrui Liu and Zhenbo Luo and Heng Qu and Changjian Xu and Ming Xu and Jian Luan},
year={2026},
eprint={2610.23279},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.23279}
}