UniAR Uses Single Discrete Tokenizer to Unify Visual Understanding and Generation

UniAR replaces the common two-tokenizer setup in multimodal models with a single discrete visual tokenizer using multi-level feature fusion and lookup-free bitwise quantization, allowing the model to interpret its own generated tokens without re-encoding. The approach aims to unify understanding and generation in one shared representation space.