Ettin Suite: SoTA Paired Encoders and Decoders

NeoMME: an efficient Multimodal-native and Multilingual Encoder

Transformer-based Encoder-Decoder Models

Superposed Latent Autoencoder

The paper introduces the Superposed Latent Autoencoder (SLAE), a method that stores multiple wide latent representations together by superposing them into a single memory tensor using learned codes and randomized keys. SLAE eliminates the need for tight dimensional bottlenecks, achieving up to 56% lower reconstruction error on datasets such as CIFAR-10/100 and SVHN while maintaining the same storage budget. The approach also boosts downstream classification performance by up to 16.79 percentage points, demonstrating that wide representations can be effectively compressed through structured interference rather than dimensional reduction.

Introducing Gemma 4 12B: a unified, encoder-free multimodal model

T5Gemma: A new collection of encoder-decoder Gemma models

Introducing T5Gemma, a new collection of encoder-decoder LLMs.

SigLIP 2: A better multilingual vision language encoder