Rumor has it that OpenAI is already going that way. There's a technique of repeatedly looping through several neural layers that has the same effect as chain-of-thought, but without the efficiency loss of translating out to human-readable tokens, and some of OpenAI's statements about their latest model seem to fit well with that.
There was a paper posted in some thread here a while ago. Basically instead text based llm you turn the text into an image and use that as input and have the model work with the resulting matrices. This ended up as you'd guess, faster/more efficient/generally better in all their benchmarks compared to text string based llm.
what would be the benefit of turning it into an image rather than some arbitrary representation?
I'm not sure exactly. Maybe its just easier to work with matrix data. That's all an image is anyhow. The imaging is just to convert the text to some matrix that's tied to the text structure.
seems like a bad UX decision, unless it is somehow summarized at the end or something
it doesn't seem necessary to read a full CoT exchange. rather a final graph of why a decision was made would be ideal for my usage.
It's already impossible for end users to read the thinking output of OpenAI's models.