[D] Thought Space in LLMs?

I'm an amateur hobbyist around AI concepts. I'm curious to know what the obstacles for the following implementation could be and to what extent it has been tried.

Image generation models use an iterative approach of noise reduction to generate an image.

Could we create a thought-space representation of text, and use a similar technique to crystallize thoughts from random noise?

We could create a long length embeddings model that transforms paragraphs or even documents into long vectors, or possibly …

concepts create generate image image generation image generation models implementation iterative llms machinelearning noise obstacles representation space text thought