This paper introduces a generative diffusion model to discover new periodic orbits in 2D Navier-Stokes equations, even when trained on non-periodic turbulent data. By modifying the model and enforcing symmetries, it generated plausible candidates which were then refined into 111 previously unknown exact solutions, highlighting generative AI's role as a complementary tool for exploring complex solution spaces.

This paper introduces Brain-Gen, a transformer-based framework that extracts spatio-temporal representations from EEG signals to reconstruct visual stimuli using Latent Diffusion Models. The method significantly improves clustering accuracy and generalization across unseen classes, advancing the semantic interpretation of EEG signals.

This paper introduces Reward Forcing, a novel framework for efficient streaming video generation that tackles issues like diminished motion dynamics and over-reliance on initial frames. It achieves state-of-the-art performance by combining EMA-Sink for improved long-term context and Rewarded Distribution Matching Distillation (Re-DMD) to enhance motion quality.