This source introduces BAGEL, a large multimodal model designed for unified image understanding and generation. It discusses the model's Mixture-of-Transformer-Experts (MoT) architecture, highlighting its bottleneck-free designwhich enables better long-context interaction and scaling. The document details the diverse training data, including text, image-text pairs, and interleaved video and web content. BAGEL demonstrates strong performance on various benchmarks, with distinct learning patterns observed for different tasks, and shows emergent capabilities as training progresses, particularly in complex image editing scenarios. The paper also includes qualitative comparisons and discusses current limitations and future directions for multimodal models.

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