LatentMorph teaches an image-making AI to quietly think in its head while it draws, instead of stopping to write out its thoughts in words.
This paper argues that true world models are not just sprinkling facts into single tasks, but building a unified system that can see, think, remember, act, and generate across many situations.
The paper shows how to make AI think faster and smarter by planning in a hidden space instead of writing long step-by-step sentences.
LaViT is a new way to teach smaller vision-language models to look at the right parts of an image before they speak.
Fast-ThinkAct teaches a robot to plan with a few tiny hidden "thought tokens" instead of long paragraphs, making it much faster while staying smart.
Language is lumpy: easy stretches and tricky jumps are mixed together, but old models spend the same effort on every word.