Jingkang Yang

AURORA-LM achieves state-of-the-art performance in language generation by leveraging a continuous-latent diffusion framework that preserves text fidelity while enhancing generative capabilities.

Forget dialogue summaries – FileGram builds user profiles directly from atomic file-system actions, unlocking a richer, more privacy-preserving approach to agent personalization.

Today's best AI agents can only achieve 48% accuracy when reasoning about your personal files, revealing a surprising gap between lab performance and real-world usability.