Jingkang Yang

Rubric-based scoring reveals that vision-language models can significantly improve their grounding in visual evidence, enhancing reasoning and instruction adherence.

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.

Turns out, just showing a vision-language model the last few frames of a video stream beats fancy architectures designed for long-term memory.