Ying Nian Wu

This study shows continuous attractor networks can simultaneously compute model evidence and posterior sampling in distinct subspaces, advancing understanding of neural computation.

This paper introduces a framework for optimizing reasoning chains in LLMs by modeling them as sequential decisions in a latent space, showing improved efficiency and interpretability.

This paper proposes a neural circuit model that generates temporally scalable sequences through Lie group principles, showing consistency with experimental data.