Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation
Unified multimodal models (UMMs) provide an interface for interleaved text-image generation, where generated visual states can directly inform subsequent reasoning and synthesis. However, existing systems are still primarily demonstrated on short trajectories or simplified visual content. We develop a large UMM natively trained on interleaved multimodal data, with strong capabilities in image synthesis, text rendering, complex composition, and multimodal planning, and study how to extend these native capabilities to substantially longer autoregressive trajectories. The main challenge is that generated images continuously accumulate in context: irrelevant or imperfect visual states interfe...
| Authors | Ziqiao Peng, Yutao Cui, Wang Lin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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