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KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs

Intermediate

Yixuan Tang, Yi YangJan 3arXiv

This paper shows how to get strong text embeddings from decoder-only language models without any training.

#text embeddings#decoder-only LLMs#causal attention

Next-Embedding Prediction Makes Strong Vision Learners

Beginner

Sihan Xu, Ziqiao Ma et al.Dec 18arXiv

This paper introduces NEPA, a very simple way to teach vision models by having them predict the next patch’s embedding in an image sequence, just like language models predict the next word.

#self-supervised learning#vision transformer#autoregression

SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder

Intermediate

Minglei Shi, Haolin Wang et al.Dec 12arXiv

This paper shows you can train a big text-to-image diffusion model directly on the features of a vision foundation model (like DINOv3) without using a VAE.

#text-to-image#diffusion transformer#flow matching

Papers3

KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs

Next-Embedding Prediction Makes Strong Vision Learners

SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder