Mengdan Zhu

VLMs can achieve superior visual reasoning by dynamically decomposing queries, extracting premise-conditioned visual latents, and reasoning through grounded rationales, outperforming even multimodal CoT methods.

Scaling laws hold for interest modeling: bigger LLMs and more inference-time sampling consistently boost news recommendation quality, and can be distilled into smaller, deployable models.