Related: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces![1]

> Our findings consistently challenge the prevailing narrative that intermediate tokens constitute a semantically meaningful reasoning process. First, we observe a pronounced lack of correlation between solution correctness and trace validity—models frequently produce invalid reasoning traces even when they arrive at correct solutions. Second, and more strikingly, models trained on corrupted or semantically irrelevant traces achieve performance comparable to, and often exceeding, that of models trained on correct traces, especially on out-of-distribution tasks.

[1]: https://arxiv.org/abs/2504.09762

Over time, I've learned to accept that many people -- even very clever ones -- are incapable of holding a metaphor at arm's length. Once they accept the words of a metaphor as applicable at all, the metaphor collapses entirely into literalism for them. They can no longer see that the metaphor was just a tool with inherent limitatation and boundaries.

Because the field of artificial "intelligence" is constructed around the idea of applying psychological metaphors to computational systems (a very powerful idea!) it's almost a worst case scenario for these people.

Suddenly, they're reversing the metaphors and applying computational schema to psychological processes ("aren't we really just stochastic parrots ourselves?!"); or, like here, they find themselves surprised and confused when they stumble across the natural boundaries of the metaphor experimentally.

It's because they never had sight of the boundaries in the first place and maybe never can quite see them. The words only make sense to them as literal equivalence, and so their surprise when they run into stuff like this is earnest and deep.

First author here, surprised to see the paper in HN! :)

When doing the paper we noticed that models are very good at generating post-hoc plausible CoT, which to me knowledge can happen quite often with relatively easy tasks.

You might be interested in reading this other paper that came out after ours: https://arxiv.org/abs/2507.05246

Natural intelligences do this too

It's... complicated. Yes, RL reward hacking makes it learn "bird language" and yes, reasoning traces can be misleading. However they also pretty clearly steer the final reply and not simply justify it, and can stay somewhat coherent and relevant with readability SFT and rewards. All these phenomenas coexist, they aren't mutually exclusive. Reasoning traces are still useful for debugging.

that sounds testable - if you skip the reasoning tokens, do you get the same result?

if not, then there's certainly some bearing, but not necessarily in how we read the tokens as text