Update README.md

README.md

@@ -48,6 +48,28 @@ This repository hosts the model weights for **BAGEL**. For installation, usage i
 
 
 
+<p align="left"><img src="https://github.com/ByteDance-Seed/Bagel/raw/main/assets/teaser.webp" width="50%"></p>
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+## 🧠 Method
+BAGEL adopts a Mixture-of-Transformer-Experts (MoT) architecture to maximize the model’s capacity to learn from richly diverse multimodal information. Following the same principle of capacity maximization, it utilizes two separate encoders to capture pixel-level and semantic-level features of an image. The overall framework follows a Next Group of Token Prediction paradigm, where the model is trained to predict the next group of language or visual tokens as a compression target.
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+BAGEL scales MoT’s capacity through Pre-training, Continued Training, and Supervised Finetuning on trillions of interleaved multimodal tokens spanning language, image, video, and web data. It surpasses open models on standard understanding and generation benchmarks and demonstrates advanced in-context multimodal abilities like free-form image editing, future frame prediction, 3D manipulation, world navigation, and sequential reasoning.
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+<p align="left"><img src="https://github.com/ByteDance-Seed/Bagel/raw/main/assets/arch.png" width="50%"></p>
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+## 🌱 Emerging Properties
+<p align="left"><img src="https://github.com/ByteDance-Seed/Bagel/raw/main/assets/emerging_curves.png" width="50%"></p>
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+As we scale up BAGEL’s pretraining with more multimodal tokens, we observe consistent performance gains across understanding, generation, and editing tasks. Different capabilities emerge at distinct training stages—multimodal understanding and generation appear early, followed by basic editing, while complex, intelligent editing emerges later. This staged progression suggests an emergent pattern, where advanced multimodal reasoning builds on well-formed foundational skills. Ablation studies further show that combining VAE and ViT features significantly improves intelligent editing, underscoring the importance of visual-semantic context in enabling complex multimodal reasoning and further supporting its role in the emergence of advanced capabilities.
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 ## 📊 Benchmarks
 ### 1. Visual Understanding
 | Model | MME ↑ | MMBench ↑ |   MMMU ↑ | MM-Vet ↑ | MathVista ↑ |