Jim Fan 发布 VFlash 视觉模型，面向 Physical AI 并行解析视觉世界

Jim Fan· @DrJimFan · X·2026-11-06 00:31· 45 分钟前AI 评分46

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Jim Fan 发布 VFlash 视觉模型，面向 Physical AI 并行解析视觉世界，在 Jetson Thor、DGX Spark 等边缘 GPU 上实现最高 12 倍更快的动作解码和 5.1 倍整体机器人推理加速。它一次性输出完整几何形状而非逐 token 解码，3D 轨迹误差最多降低 61%，精度甚至超过 VLM 主干。论文与代码均已开源。

正文 · 原文

The era of System One Vision Model is here! Introducing VFlash: parsing the visual world in parallel at unmatched speeds for Physical AI.

Most embodied prediction tasks like object localization, robot trajectory planning, and dense 3D motion are fixed-length float vectors. For example, a bounding box is always 6 tokens, a 10-waypoint robot arm trajectory is 54: we know these beforehand. Instead of decoding one token at a time, we can output the entire geometric shape in one go.

VFlash enables up to 12x faster action decoding and a 5.1x overall robot inference speedup on edge GPUs like the Jetson Thor and DGX Spark. What’s surprising is that it ends up even more accurate than the VLM backbone, with up to 61% lower 3D trajectory error. The intuition is that VFlash avoids the cascading errors of autoregressive decoding, where every coordinate is written in ink and every later point loses the chance to backspace.

Everything that moves will be autonomous, and everything autonomous will need to think on its own chip. The next inference revolution is physical reasoning at the speed of reflex.

And as always, it’s open paper, open source! Link in thread:

来源：Jim Fan · x.com