We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework that enables generative diffusion bridge modeling with fractional noise for both paired and unpaired training data.

Representational alignment based on concept discovery across ViTs trained on different tasks.

We propose a training-free visual cropping method that leverages MLLM-internal representations for VQA tasks focusing on small details, achieving strong performance with significantly higher efficiency than prior methods.

This papers introduces a Diffusion Transformer (DiT) for sampling 3D molecular conformers.

Vision Transformers (ViTs) perform object binding.