General Instinct | Any frontier model. Any edge device.
general-instinct.com · 1,878 words · saved by 1 readers
The deployment layer for physical AI. Any frontier model on any edge device, sub-100ms.
Pixel-level video prediction is the most expensive thing a World Action Model (WAM) does. This post is really about whether it is worth it. We start where the case for generation is strongest, DreamZero, which turns a 14B video-diffusion model into a robot policy, then follow the evidence from Fast-WAM and ImageWAM that the generated future is a training scaffold you can throw away at inference. From V-JEPA onward, one thread runs through all of it: maybe the latent is all you need. 1DreamZero: the strongest case for imagining the future A Vision-Language-Action model (VLA) maps what a…
saved by
related reading
- Pretrained to Imagine, Fine-Tuned to Act: The Rise of World-Action Models | NVIDIA Technical Blogdeveloper.nvidia.com
- [2603.16666] Fast-WAM: Do World Action Models Need Test-time Future Imagination?arxiv.org
- How to train a frontier-level world modelnext-state.github.io
- World Action Model Atlasjoeclinton.me
- The flavor of the bitter lesson for computer vision - Vincent Sitzmannvincentsitzmann.com
- World Models | Rohit Bandarurohitbandaru.github.io
- The First Fully General Computer Action Model | blogsi.inc
- The Model That Dreams the Worldmoe-capital.com
- Anirudha Majumdar (@Majumdar_Ani) on Xx.com
- World Models: Computing the Uncomputablenotboring.co
- 1X World Model | From Video to Action: A New Way Robots Learn1x.tech
- Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AIrhoda.ai