| title | Semantic Generative Tuning for Unified Multimodal Models |
|---|---|
| authors | Songsong Yu, Yuxin Chen, Ying Shan, Yanwei Li |
| date | 2026-05-18 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.18714 |
| affiliation | Shanghai Jiao Tong University |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy yields misaligned representation spaces, isolating visual understanding from generation and hindering their mutual reinforcement. This work presents the first systematic investigation into generative post-training, where we formulate hierarchical visual tasks as generative proxies to bridge the isolation in UMMs. Our empirical investigation reveals that high-level semantic tasks, particularly image segmentation, serve as optimal proxies. Unlike low-level tasks that distract models with texture details, segmentation provides structural semantics that significantly enhance both vision-centric perception and generative layout fidelity. Building upon these insights, we introduce Semantic Generative Tuning (SGT), a novel paradigm that leverages segmentation as a generative proxy to align and synergize multimodal capabilities. Mechanistic analyses further demonstrate that SGT fundamentally improves feature linear separability and optimizes visual-textual attention allocation pattern. Extensive evaluations show that SGT consistently improves both multimodal comprehension and generative fidelity across mainstream benchmarks. Our code is available on the https://song2yu.github.io/SGT/. |
cite
@misc{yu2026semantic,
title={Semantic Generative Tuning for Unified Multimodal Models},
author={Songsong Yu and Yuxin Chen and Ying Shan and Yanwei Li},
year={2026},
eprint={2605.18714},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.18714}
}