| title | Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification |
|---|---|
| authors | Wujian Peng, Lingchen Meng, Yuxuan Cai, Xianwei Zhuang, Yuhuan Yang, Rongyao Fang, Chenfei Wu, Junyang Lin, Zuxuan Wu, Shuai Bai |
| date | 2026-06-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.18249 |
| affiliation | Qwen |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Unified Multimodal Modeling aims to integrate visual understanding and generation within a single system. However, existing approaches typically rely on two disparate visual tokenizers, which splits the representation space and hinders truly unified modeling. We propose UniAR, a unified autoregressive framework where a single discrete visual tokenizer serves as the key bridge between understanding and generation, enabling a shared context in which the model can directly interpret its own generated visual tokens without additional re-encoding. UniAR adapts a pretrained vision encoder with multi-level feature fusion and a lookup-free bitwise quantization scheme, preserving both high-level semantics and low-level details while scaling the effective visual vocabulary at minimal cost. Building on this, the unified autoregressive model adopts parallel-bitwise-prediction to jointly predict spatially grouped, multi-level visual codes, substantially reducing visual sequence length and accelerating generation. Finally, a diffusion-based visual decoder operates on discrete visual tokens to decode high-fidelity images. Through large-scale pre-training, followed by supervised fine-tuning and reinforcement learning, UniAR achieves state-of-the-art performance on image generation and image editing while remaining competitive on multimodal understanding benchmarks. The project page is available at https://sharelab-sii.github.io/uniar-web. |
cite
@misc{peng2026unified,
title={Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification},
author={Wujian Peng and Lingchen Meng and Yuxuan Cai and Xianwei Zhuang and Yuhuan Yang and Rongyao Fang and Chenfei Wu and Junyang Lin and Zuxuan Wu and Shuai Bai},
year={2026},
eprint={2606.18249},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.18249}
}