Vernon Toh

Agents falter in partially observable settings, revealing that merely gathering visual evidence isn't enough without robust perceptual state management.

Agents can discover tool behaviors but fail to adapt effectively, often resorting to inefficient exhaustive searches in dynamic environments.

GRAIL reweights token advantages in reinforcement learning, leading to significant accuracy gains without the need for expensive process-level supervision.