Kuan Zhang

Learning to predict the visual consequences of actions significantly boosts policy performance and generalization, while simultaneously learning to generate actions sharpens world models.

Image corruptions can drastically impair retrieval performance, revealing that clean image-only queries often outperform joint retrieval in snap-and-ask scenarios.

Despite advancements in static image understanding, models falter in tracking dynamic world states over time, exposing critical gaps in spatial reasoning.