Qi Liu

A single model can now seamlessly handle over 10 diverse audio-visual tasks without the need for task-specific architectures.

Noise-aware residual correction boosts the realism of autoregressive audio-visual generation, tackling issues of identity drift and desynchronization head-on.

Cheap models can recover early evolutionary progress, enabling a shift in budget allocation that dramatically enhances LLM-driven algorithm discovery.

XAlpha revolutionizes alpha discovery by turning it into a continuous learning process that adapts and evolves based on real-time feedback.