| title | STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation |
|---|---|
| authors | Ying Shen, Tianrong Chen, Yuan Gao, Yizhe Zhang, Yuyang Wang, Miguel Ángel Bautista, Shuangfei Zhai, Joshua M. Susskind, Jiatao Gu |
| date | 2026-05-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.08029 |
| affiliation | Apple |
| upvotes | 13 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generate interleaved text-image sequences. Most existing approaches combine autoregressive language modeling with diffusion-based image generators, inheriting a structural mismatch between causal text generation and iterative visual denoising. We observe that autoregressive normalizing flows are autoregressive Transformers--sharing the same causal mask, KV-cache mechanism, and left-to-right structure as LLMs--making them the most natural paradigm for true unified multimodal generation. We present STARFlow2, built on the Pretzel architecture that vertically interleaves a pretrained VLM stream with a TarFlow stream via residual skip connections, both operating under the same causal mask. Combined with a deep-shallow flow design and a unified FAE latent space, STARFlow2 enables cache-friendly interleaved generation where both text and visual outputs directly enter the KV-cache without re-encoding. Experiments demonstrate strong performance across image generation and multimodal understanding benchmarks, validating autoregressive flows as a viable foundation for unified multimodal modeling. |
cite
@misc{shen2026starflow,
title={STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation},
author={Ying Shen and Tianrong Chen and Yuan Gao and Yizhe Zhang and Yuyang Wang and Miguel Ángel Bautista and Shuangfei Zhai and Joshua M. Susskind and Jiatao Gu},
year={2026},
eprint={2605.08029},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.08029}
}