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Quantifying the Gap between Understanding and Generation within Unified Multimodal Models

Intermediate

Chenlong Wang, Yuhang Chen et al.Feb 2arXiv

This paper shows that many AI models that both read images and write images are not truly unified inside—they often understand well but fail to generate (or the other way around).

#Unified Multimodal Models#GAPEVAL#Gap Score

Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders

Intermediate

Shengbang Tong, Boyang Zheng et al.Jan 22arXiv

Before this work, most text-to-image models used VAEs (small, squished image codes) and struggled with slow training and overfitting on high-quality fine-tuning sets.

#Representation Autoencoder#RAE#Variational Autoencoder

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision

Beginner

Ruiyan Han, Zhen Fang et al.Jan 6arXiv

This paper fixes a common problem in multimodal AI: models can understand pictures and words well but stumble when asked to create matching images.

#Unified Multimodal Models#Self-Generated Supervision#Conduction Aphasia

Papers3

Quantifying the Gap between Understanding and Generation within Unified Multimodal Models

Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision