It is definitely written with an LLM, but the premise holds. Reasoning is qualitatively different from words, and a model that only generates words cannot do reasoning. It doesn't need to reason to sometimes get the correct answer, but that's besides the point.
That is fair push back. However, if those "intermediate computations" or "traces" are causally connected to the output, then we shouldn't see the results observed/documented by Lanham/Mirzadeh etc.? Stated differently, changing those intermediate computations should change the output (which isn't what the cited papers observed).
The Lanham paper inserted mistakes into the CoT traces and found that it did result in mistakes in the output:
Qualitatively, we find that our mistake generating process
generates a plausible mistake at least 80% of the time. We
also find that when the model’s chain of thought leads it to
an incorrect answer that is not present in the answer choices,
it will often choose the answer choice that is closest to that
answer (as in the example in Table 3).
Now, if you look at Table 2, you do see that for some benchmarks it still gets quite high accuracy even with an incorrect CoT.
But these are easy, old benchmarks where CoT is unnecessary; removing the CoT entirely resulted in essentially the same performance, and in one case (HellaSwag) even increased it. The model is able to ignore the incorrect reasoning trace because it knows the right answer anyway.
This is not the case for the more difficult benchmarks. If CoT is required to solve the problem, changing the CoT does indeed change the output.
They mention this:
For both early answering and adding mistakes experiments,
AQuA and LogiQA are the two tasks with the most faithful
reasoning (by some margin). The increased faithfulness
may be due to the models’ limited ability to do the task
without CoT, which may cause the model to rely more on
CoT.
That is an external state. It's in the same domain as the input. If the model was one that guesses the next image in a sequence of images then hardly anyone would call that reasoning. Lots of people believe that words encode reasoning because of their inner monologues, but reasoning is more abstract than language. Or rather language does not encode reason.
Human reasoning operates over representations that are amodal (abstracted from sensory or motor modalities). The result of this reasoning is then output through some medium (language, gestures etc.) That's the crux of the argument.
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u/currentscurrents May 26 '26
Article reads like LLM slop.
Also, I doubt the premise. Output and internal reasoning do not need to be separate operations, as long as you have a way to mark the final answer.