arxivcs.CVcs.AI2026-07-09
LEEVLA: Seeing What Matters in Latent Environment Evolution for Vision-Language-Action
Qi Lyu, Baicheng Liu, Xudong Wang, Jiahua Dong, Lianqing Liu, Zhi Han
Vision-language-action (VLA) models aim to map multimodal inputs to robot actions. However, most existing approaches struggle to cover complex dynamic scenarios due to treating all visual tokens uniformly and reasoning with human-selected factors, which lack mechanisms to emphasi…