We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a novel, pure decoder-only transformer architecture. Our framework uniquely eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference, leading to significant efficiency gains, especially for high-resolution inputs. This is achieved through a modality-specific Mixture-of-Experts (MoE) structure trained with a single autoregressive (AR) objective, which also natively supports dynamic resolutions. Furthermore, we pioneer a multi-scale visual autoregressive mechanism within the Large Language Model (LLM) that drastically reduces decoding steps compared to diffusion-based methods while maintaining state-of-the-art performance. Our findings demonstrate the powerful potential of pure autoregressive modeling as a sufficient and elegant foundation for unified multimodal intelligence. As a result, OneCAT sets a new performance standard, outperforming existing open-source unified multimodal models across benchmarks for multimodal generation, editing, and understanding.
@article{arxiv.2509.03498,
title = {OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation},
author = {Han Li and Xinyu Peng and Yaoming Wang and Zelin Peng and Xin Chen and Rongxiang Weng and Jingang Wang and Xunliang Cai and Wenrui Dai and Hongkai Xiong},
journal= {arXiv preprint arXiv:2509.03498},
year = {2025}
}