Did you try it with high reasoning effort?
“Did you try running it over and over until you got the results you wanted?”
One thing it's hard to wrap my head around is that we are giving more and more trust to something we don't understand with the assumption (often unchecked) that it just works. Basically your refrain is used to justify all sorts of odd setup of AIs, agents, etc.
I am much more worried about the problem where LLMs are actively misleading low-info users into thinking they’re people, especially children and old people.
As one might expect, because the AI isn't actually thinking, it's just spending more tokens on the problem. This sometimes leads to the desired outcome but the phenomenon is very brittle and disappears when the AI is pushed outside the bounds of its training.
To quote their discussion, "CoT is not a mechanism for genuine logical inference but rather a sophisticated form of structured pattern matching, fundamentally bounded by the data distribution seen during training. When pushed even slightly beyond this distribution, its performance degrades significantly, exposing the superficial nature of the “reasoning” it produces."
This is science at its worst, where you start at an inflammatory conclusion and work backwards. There is nothing particularly novel presented here, especially not in the mathematics; obviously performance will degrade on out-of-distribution tasks (and will do so for humans under the same formulation), but the real question is how out-of-distribution a lot of tasks actually are if they can still be solved with CoT. Yes, if you restrict the dataset, then it will perform poorly. But humans already have a pretty large visual dataset to pull from, so what are we comparing to here? How do tiny language models trained on small amounts of data demonstrate fundamental limitations?
I'm eager to see more works showing the limitations of LLM reasoning, both at small and large scale, but this ain't it. Others have already supplied similar critiques, so let's please stop sharing this one around without the grain of salt.
Science starts with a guess and you run experiments to test.
I honestly wish this paper actually showed what it claims, since it is a significant open problem to understand CoT reasoning relative to the underlying training set.
What would be your argument against
1. COT models performing way better in benchmarks than normal models
2. people choose to use the COT models in day to day life because they actually find that it gives better performance
I know what you're saying here, and I know it is primarily a critique of my phrasing, but establishing something like this is the objective of in-context learning theory and mathematical applications of deep learning. It is possible to prove that sufficiently well-trained models will generalize for certain unseen classes of patterns, e.g. transformer acting like gradient descent. There is still a long way to go in the theory---it is difficult research!
> performance collapses under modest distribution shift
The problem is that the notion of "modest" depends on the scale here. With enough varied data and/or enough parameters, what was once out-of-distribution can become in-distribution. The paper is purposely ignorant of this fact. Yes, the claims hold for tiny models, but I don't think anyone ever doubted this.
Regardless of being in conversation or thinking context this doesn't prevent the model from speaking the wrong answer so the paper on the illusion of thinking makes sense.
What actually seems to be happening is a form of conversational prompting. Of course with the right conversation back and forth with an LLM you can inject knowledge in a way that causes the natural distribution to shift (again - side effect of the LLM tech.) but by itself it won't naturally get the answer perfect every time.
If this extended thinking were actually working you would expect the LLM to be able to logically conclude an answer with very high accuracy 100% of the time which it does not.
- https://arcprize.org/leaderboard
- https://aider.chat/docs/leaderboards/
- https://arstechnica.com/ai/2025/07/google-deepmind-earns-gol...
Surely the IMO problems weren't "within the bounds" of Gemini's training data.
Certainly they weren't training on the unreleased problems. Defining out of distribution gets tricky.
This is false.
https://x.com/YiTayML/status/1947350087941951596
This is false even for the OpenAI model
https://x.com/polynoamial/status/1946478250974200272
"Typically for these AI results, like in Go/Dota/Poker/Diplomacy, researchers spend years making an AI that masters one narrow domain and does little else. But this isn’t an IMO-specific model. It’s a reasoning LLM that incorporates new experimental general-purpose techniques."
GPT 5 fast gets many things wrong but switching to the thinking model fixes the issues very often.
I skimmed through the paper and I didnt see any mention of what parameters they used other than they use gpt-5 via the API.
What was the reasoning_effort? verbosity? temperature?
These things matter.
So it makes sense to me that you should try until you get the results you want (or fail to do so). And it makes sense to ask people what they've tried. I haven't done the work yet to try this for gpt5 and am not that optimistic, but it is possible it will turn out this way again.
Maybe I’m misunderstanding, but it sounds like you’re framing a completely normal proces (try, fail, adjust) as if it’s unreasonable?
In reality, when something doesn’t work, it would seem to me that the obvious next step is to adapt and try again. This does not seem like a radical approach but instead seems to largely be how problem solving sort of works?
For example, when I was a kid trying to push start my motorcycle, it wouldn’t fire no matter what I did. Someone suggested a simple tweak, try a different gear. I did, and instantly the bike roared to life. What I was doing wasn’t wrong, it just needed a slight adjustment to get the result I was after.
1. this is magic and will one-shot your questions 2. but if it goes wrong, keep trying until it works
Plus, knowing it's all probabilistic, how do you know, without knowing ahead of time already, that the result is correct? Is that not the classic halting problem?
> 1. this is magic and will one-shot your questions 2. but if it goes wrong, keep trying until it works
Ah that makes sense. I forgot the "magic" part, and was looking at it more practically.
For LLMs none of it sticks. You keep “teaching” it and the next time it forgets everything.
So again you keep trying until you get the results you want, which you need to know ahead of time.
"Did you try a room full of chimpanzees with typewriters?"