Figure 1. (Left) Performance on MMLU (language ability) across composable pretraining stages (LM → +MMU → +T2I). Standard MoE and structurally-isolated MoT suffer catastrophic routing collapse upon integrating continuous generative objectives. Rosetta maintains a stable semantic anchor throughout all stages. (Right) Qualitative image generation results from the Rosetta model.

Figure 2. Rosetta FFN. Three mechanisms enable non-destructive modality expansion: (1) Unified Attention — globally shared QKV projections preserve dense cross-modal interactions. (2) Composable FFN — modality-specific plug-and-play experts (Text / ViT / VAE) are bridged by a single Global Shared Expert that anchors foundational knowledge. (3) Conflict-Free Optimization (MAOP) — surgically neutralizes destructive gradients with zero memory overhead.