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The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

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Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#51

Post by sitkack »

sneak wrote: post #44
8bitsrule wrote: post #36

Fusion has been 25 years away for all of my life.

Fusion is net positive energy now; that happened in 2022 (+54%). In 2025 they got a 313% gain (4.13 output factor). Fusion is actually here and working. It’s not cost effective yet but to pretend there has been no progress or achievements is fundamentally false.

Negative Negs spit out low effort snark, they said the same thing about solar, electric cars, even multicore, jit, open source. Thanks for refuting them, the forum software itself should either quarantine the response or auto respond before the comment is submitted. These people don't build the future.

Fusion News, May 28th, 2025 https://www.youtube.com/watch?v=1YHcI-SfKx8

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#52

Post by antics »

sitkack wrote: post #46
antics wrote: post #45

I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. And the question they are collectively trying to ask is whether this will continue forever. I've never seen this question quantified in a really compelling way, and while interesting, I'm not sure this PDF succeeds, at least not well-enough to silence dissent. I think AI maximalists will contin…

There is no reason that omniscient-yet-dim-witted has to plateau at human intelligence.

I am not sure if you mean this to refute something in what I've written but to be clear I am not arguing for or against what the authors think. I'm trying to state why I think there is a disconnect between them and more optimistic groups that work on AI.

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#53

Post by MoonGhost »

roenxi wrote: post #38
actinium226 wrote: post #24

Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things

What do you think has changed? The situation is still about as promising for AGI in a few years - if not more so. Papers like this are the academics mapping out where the engineering efforts need to be directed to get there and it seems to be a relatively small number of challenges that are easier as the ones already overcome - we know machine learning can solve Towers of Hanoi, for example. It isn't fundamentally co…

> What do you think has changed? The situation is still about as promising for AGI in a few years - if not more so

It's better today. Hoping that LLMs can get us to AGI in one hop was naive. Depending on definition of AGI we might be already there. But for superhuman level in all possible tasks there are many steps to be done. The obvious way is to find a solution for each type of tasks. We have already for math calculations, it's using tools. Many other types can be solved the same way. After a while we'll gradually get to well rounded 'brain', or model(s) + support tools.

So, so far future looks bright, there is progress, problems, but not deadlocks.

PS: Turing test is a nobody seriously talks about today.

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#54

Post by lrhegeba »

sneak wrote: post #44
8bitsrule wrote: post #36

Fusion has been 25 years away for all of my life.

Fusion is net positive energy now; that happened in 2022 (+54%). In 2025 they got a 313% gain (4.13 output factor). Fusion is actually here and working. It’s not cost effective yet but to pretend there has been no progress or achievements is fundamentally false.

It isnt when you look at Q total. Total energy input for all needed support systems versus energy produced. See https://en.wikipedia.org/wiki/Fusion_energy_gain_factor for more details

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#55

Post by esafak »

th0ma5 wrote: post #48
esafak wrote: post #47

I don't know that I would call it an "illusion of thinking", but LLMs do have limitations. Humans do too. No amount of human thinking has solved numerous open problems.

The errors that LLMs make and the errors that people make are not probably not comparable enough in a lot of the discussions about LLM limitations at this point?

We have different failure modes. And I'm sure researchers, faced with these results, will be motivated to overcome these limitations. This is all good, keep it coming. I just don't understand the some of the naysaying here.

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#56

Post by drodgers »

antics wrote: post #52
sitkack wrote: post #46

Earlier quoted context omitted.

There is no reason that omniscient-yet-dim-witted has to plateau at human intelligence.

I am not sure if you mean this to refute something in what I've written but to be clear I am not arguing for or against what the authors think. I'm trying to state why I think there is a disconnect between them and more optimistic groups that work on AI.

I think that commenter was disagreeing with this line:

> because omniscient-yet-dim-witted models terminate at "superhumanly assistive"

It might be that with dim wits + enough brute force (knowledge, parallelism, trial-and-error, specialisation, speed) models could still substitute for humans and transform the economy in short order.

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#57

Post by sitkack »

drodgers wrote: post #50
antics wrote: post #45

I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. And the question they are collectively trying to ask is whether this will continue forever. I've never seen this question quantified in a really compelling way, and while interesting, I'm not sure this PDF succeeds, at least not well-enough to silence dissent. I think AI maximalists will contin…

> I think AI maximalists will continue to think that the models are in fact getting less dim-witted I'm bullish (and scared) about AI progress precisely because I think they've only gotten a little less dim-witted in the last few years, but their practical capabilities have improved a lot thanks to better knowledge, taste, context, tooling etc. What scares me is that I think there's a reasoning/agency capabilities ov…

I think you are right, and that the next step function can be achieved using the models we have, either by scaling the inference, or changing the way inference is done.

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#58

Post by ngneer »

AlienRobot wrote: post #25
ivape wrote: post #6

This is easily explained by accepting that there is no such thing as LRMs. LRMs are just LLMs that iterate on its own answers more (or provides itself more context information of a certain type). The reasoning loop on an "LRM" will be equivalent to asking a regular LLM to "refine" its own response, or "consider" additional context of a certain type. There is no such thing as reasoning basically, as it was always a me…

Is that what "reasoning" means? That sounds pretty ridiculous. I've thought before that AI is as "intelligent" as your smartphone is "smart," but I didn't think "reasoning" would be just another buzzword.

I am not too familiar with the latest hype, but "reasoning" has a very straightforward definition in my mind. For example, can the program in question derive new facts from old ones in a logically sound manner. Things like applying modus ponens. (A and A => B) => B. Or, all men are mortal and Socrates is a man, and therefore Socrates is mortal. If the program cannot deduce new facts, then it is not reasoning, at least not by my definition.

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#59

Post by rafterydj »

bayindirh wrote: post #29
bicepjai wrote: post #21

The study challenges the assumption that more “thinking” or longer reasoning traces necessarily lead to better problem-solving in LRMs

As a test, I asked Gemini 2.5 Flash and Gemini 2.5 Pro to decode a single BASE64 string. Flash answered correctly in ~2 seconds, at most. Pro answered very wrongly after thinking and elaborating for ~5 minutes. Flash was also giving a wrong answer for the same string in the past, but it improved. Prompt was the same: "Hey, can you decode $BASE64_string?" I have no further comments.

well that's not a very convincing argument. That's just a failure to recognize when the use of a tool- base64 decoder- is needed, not a reasoning problem at all, right?

Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]

#60

Post by georgemcbay »

tonyhart7 wrote: post #33
actinium226 wrote: post #24

Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things

I think we just around at 80% of progress the easy part is done but the hard part is so hard it takes years to progress

> the easy part is done but the hard part is so hard it takes years to progress

There is also no guarantee of continued progress to a breakthrough.

We have been through several "AI Winters" before where promising new technology was discovered and people in the field were convinced that the breakthrough was just around the corner and it never came.

LLMs aren't quite the same situation as they do have some undeniable utility to a wide variety of people even without AGI springing out of them, but the blind optimism that surely progress will continue at a rapid pace until the assumed breakthrough is realized feels pretty familiar to the hype cycle preceding past AI "Winters".

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