I feel like the current “reasoning” that LLMs are doing has got to be a dead end eventually. Every time I have to read another answer with “but wait” and “Actually,” as they “reason” their way to a (sometimes) better answer, I feel like there’s got to be a way to just shortcut to the actual correct answer instead of burning all these token going in circles mimicking actual thought

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nsingh2 5 hours ago | parent | next [-]

One line of evolution seems to be toward some form of latent-space reasoning, as in [1]. Natural language seems like a relatively low-bandwidth channel for intermediate reasoning.

[1] https://github.com/sapientinc/HRM-Text

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neuroticnews25 2 hours ago | parent | prev | next [-]

I was pretty happy with Depseek Pro in Opencode util I discovered I can see the thinking trace by clicking on the "thinking..." communicate. All those seemingly unnecessary "but wait" messages are frustrating to read. But at least to some extent it's just model taking time thinking through the problem, and the trace produced doesn't have to be representative of what happens internally: https://arxiv.org/abs/2404.15758

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russfink 6 hours ago | parent | prev | next [-]

It “thinks out loud” to populate its token space. Asking it to shortcut risks truncating that process.

	▲	jauntywundrkind 5 hours ago \| parent [-]
		Yeah. It's "thinking" in absurd massive vectors. It needs to assess a couple to weigh out. That's the compression. That's the nature. It looks ridiculous when thinking traces render out such simple statements ('reassessing ..') but I expect this is far deeper an assessment than it can fully reflect to us on, and I expect its a huge part of their thinking.

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suprjami 5 hours ago | parent | prev | next [-]

So-called "caveman" thinking attempts to address this.

The important part of "actually wait, I really need to XYZ" is just "XYZ".

The model can attend to just "do XYZ" and produce almost the same vector modifications as full verbose "reasoning".

	▲	vanviegen an hour ago \| parent [-]
		I don't think that's true. If a context contains a statement followed by something opposing that statement, that will confuse the model. So "actually wait, I really need to" is there to signal that the previous thinking may be flawed and that what follows is a new attempt. It's good to remember that LLMs have no more state then what they can derive from the context up til any point. So if that context is hard to interpret, that will reduce effectiveness.

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frabcus 4 hours ago | parent | prev | next [-]

I hated it at first too...

Now though I'm considering all the hidden "thinking" in the models layers that happens for each token output. It is a wild amount of waste! We just can't see it.

This kind of stupid excessive computation is fundamentally how these models are so good.

One day hopefully not so soon someone smart or a foundation model will come up with a more efficient architecture. That's when things get really scary.

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NewJazz 5 hours ago | parent | prev | next [-]

Hardcode their "thoughts" in your agents.md... But they might still reason through it anyway.

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CamperBob2 3 hours ago | parent | prev [-]

Chain-of-thought output shouldn't be taken literally. The tokens are a substrate for computation, not necessarily evidence that the model is wasting time and electricity by gratuitously second-guessing itself over and over.

You can see evidence of this phenomenon in models dating back to the OG Deepseek R1. It was common to see the model talk itself out of the correct solution in the <thinking> block, or fail to reach it at all, only to produce a correct answer in the response. And vice versa; it was also common to see it reason its way to the right answer and then fail to follow through in the response.