Yichen Zhang^1*, Da Peng^2*, Zonghao Guo^1†, Zijian Zhang³, Xuesong Yang³,

Tong Sun³, Shichu Sun³, Yidan Zhang³, Yanghao Li¹, Haiyan Zhao¹, Wang Xu¹,

Qi Shi¹, Yangang Sun¹, Chi Chen¹, Shuo Wang¹, Yukun Yan¹, Xu Han¹,

Qiang Ma¹, Wei Ke², Liang Wang³, Zhiyuan Liu¹, Maosong Sun¹

¹Tsinghua University, ²Xi'an Jiaotong University, ³University of Chinese Academy of Sciences

* Equal contribution † Corresponding author

A recent cutting-edge topic in multimodal modeling is to unify visual comprehension and generation within a single model. However, the two tasks demand mismatched decoding regimes and visual representations, making it non-trivial to jointly optimize within a shared feature space. In this work, we present Cheers, a unified multimodal model that decouples patch-level details from semantic representations, thereby stabilizing semantics for multimodal understanding and improving fidelity for image generation via gated detail residuals. Cheers includes three key components: (i) a unified vision tokenizer that encodes and compresses image latent states into semantic tokens for efficient LLM conditioning, (ii) an LLM-based Transformer that unifies autoregressive decoding for text generation and diffusion decoding for image generation, and (iii) a cascaded flow matching head that decodes visual semantics first and then injects semantically gated detail residuals from the vision tokenizer to refine high-frequency content. Experiments on popular benchmarks demonstrate that Cheers matches or surpasses advanced UMMs in both visual understanding and generation. Notably, Cheers outperforms the Tar-1.5B on the popular benchmarks GenEval and MMBench, while requiring only 20% of the training cost, indicating effective and efficient (i.e., 4x token compression) unified multimodal modeling.