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Comment by hatthew | Hacker News Reader

My guess is that in the near* future, reasoning will no longer happen in a way that can be neatly decoded as human language.

*near meaning single digit years, which is far for AI I guess

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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.

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.

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.

It's a totally different technique though than what parent is referring to. The one you are referring to is used to take advantage of image and video compression algorithms

No, layer looping increases effective depth, but it still has to go through decode. So it's more like they increased number of layers from 100 to 200 without increasing number of parameters.

"Latent reasoning" is rather trivial - you can just replace unembed-embed step with a MLP. But labs don't do that largely because they want to read the output of unembed.

The additional layers provide additional computation without going through one or more dec/enc cycles in between. Whether or not that impacts interpretability of the final token stream depends entirely on the maximum depth permitted (and how efficient the model in question is).

Can human reasoning always be neatly decoded as language? I have an intuition it can't but it's hard to put into words.

work on your vocabulary by reading good books.

Is there a good book where I can learn the words to describe the color blue, to a blind person?

Some things are entirely outside of language. Language usually works fine only because most words are encodings of thought patterns that are already present in both parties.

Does an LLM know what blue is? A multimodal LLM probably does, because it has encoders for non-language tokens!

That's fine, we just ask them to decode it back in to human language.

and they can explain it in whichever why they like

"Let there be light" == Rendering simulation with constant speed that defines physics of time, space, matter down to the subatomic scale.

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