Long Ma

Expressiveness preservation in speech-to-speech translation remains a significant hurdle, with systems scoring poorly on emotional and nonverbal fidelity despite achieving high translation accuracy.

Fine-grained visual dependencies can drastically improve multimodal reasoning accuracy in mathematical problem-solving, challenging the notion that visual inputs are merely auxiliary.

Freezing a Sparse Autoencoder's encoder creates a reusable "safety dictionary" that generalizes to new risks in text-to-image diffusion models, offering a more robust alternative to fixed-layer steering.