this post was submitted on 08 Jun 2025
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"even when we provide the algorithm in the prompt—so that the model only needs to execute the prescribed steps—performance does not improve"
That indicates that this particular model does not follow instructions, not that it is architecturally fundamentally incapable.
Not "This particular model". Frontier LRMs s OpenAI’s o1/o3,DeepSeek-R, Claude 3.7 Sonnet Thinking, and Gemini Thinking.
The paper shows that Large Reasoning Models as defined today cannot interpret instructions. Their architecture does not allow it.
those particular models. It does not prove the architecture doesn't allow it at all. It's still possible that this is solvable with a different training technique, and none of those are using the right one. that's what they need to prove wrong.
this proves the issue is widespread, not fundamental.
Is "model" not defined as architecture+weights? Those models certainly don't share the same architecture. I might just be confused about your point though
It is, but this did not prove all architectures cannot reason, nor did it prove that all sets of weights cannot reason.
essentially they did not prove the issue is fundamental. And they have a pretty similar architecture, they're all transformers trained in a similar way. I would not say they have different architectures.
Ah, gotcha
The architecture of these LRMs may make monkeys fly out of my butt. It hasn't been proven that the architecture doesn't allow it.
You are asking to prove a negative. The onus is to show that the architecture can reason. Not to prove that it can't.
that's very true, I'm just saying this paper did not eliminate the possibility and is thus not as significant as it sounds. If they had accomplished that, the bubble would collapse, this will not meaningfully change anything, however.
also, it's not as unreasonable as that because these are automatically assembled bundles of simulated neurons.
This paper does provide a solid proof by counterexample of reasoning not occuring (following an algorithm) when it should.
The paper doesn't need to prove that reasoning never has or will occur. It's only demonstrates that current claims of AI reasoning are overhyped.
It does need to do that to meaningfully change anything, however.
Other way around. The claimed meaningful change (reasoning) has not occurred.
Meaningful change is not happening because of this paper, either, I don't know why you're playing semantic games with me though.
I'm trying to highlight the goal of this paper.
This is a knock them down paper by Apple justifying (to their shareholders) their non investment in LLMs. It is not a build them up paper trying for meaningful change and to create a better AI.
That's not the only way to make meaningful change, getting people to give up on llms would also be meaningful change. This does very little for anyone who isn't apple.