Xufang Luo

LVLMs aren't all that they seem: some lean heavily on language priors while others genuinely fuse vision and language, revealing surprisingly diverse strategies across model families.

Forget monolithic models: pMoE shows that ensembling diverse expert prompts within a single model framework yields surprisingly large gains in visual adaptation across a wide range of tasks.

LLM agents can learn to explore novel states and generalize to new tasks with a hybrid on- and off-policy RL framework that leverages memory.