Luke J. Huang — accepted AI-conference papers

2026

ForeAct: Steering Your VLA with Efficient Visual Foresight Planning

Zhuoyang Zhang, Shang Yang, Qinghao Hu, Luke J. Huang, James Hou, Yufei Sun +2

CVPR 2026

Vision-Language-Action (VLA) models convert high-level language instructions into concrete, executable actions, a task that is especially challenging in open-world environments. We present Visual Foresight Planning (ForeAct), a general and efficient planner that guides a VLA step-by-step using imagi

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2026

Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation

Zhuoyang Zhang, Luke J. Huang, Chengyue Wu, Shang Yang, Kelly Peng, Yao Lu +1

ICLR 2026oral

We present Locality-aware Parallel Decoding (LPD) to accelerate autoregressive image generation. Traditional autoregressive image generation relies on next-patch prediction, a memory-bound process that leads to high latency. Existing works have tried to parallelize next-patch prediction by shifting…

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