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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]

#241
post #71

Earlier quoted context omitted.

> I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. We keep assigning adjectives to this technology that anthropomorphize the neat tricks we've invented. There's nothing "omniscient" or "dim-witted" about these tools. They have no wit. They do not think or reason. All Large "Reasoning" Models do is generate data that they use as context to gen…

> There's nothing "omniscient" or "dim-witted" about these tools. They have no wit. They do not think or reason. > All Large "Reasoning" Models do is generate data that they use as context to generate the final answer. I.e. they do real-time tuning based on synthetic data. I always wonder when people make comments like this if they struggle with analogies. Or if it's a lack of desire to discuss concepts at different…

Language matters. Using language that accurately describes concepts and processes is important. It might not matter to a language model which only sees patterns, but it matters to humans.

So when we label the technical processes and algorithms these tools use as something that implies a far greater level of capability, we're only doing a disservice to ourselves. Maybe not to those of us who are getting rich on the market hype that these labels fuel, but certainly to the general population that doesn't understand how the technology works. If we claim that these tools have super-human intelligence, yet they fail basic tasks, how do we explain this? More importantly, if we collectively establish a false sense of security and these tools are adopted in critical processes that human lives depend on, who is blamed when they fail?

> This statement is factually correct and yet the equivalent of describing a computer as nothing more than a collection of gates and wires so shouldn't be discussed at a higher level of abstraction.

No, because we have descriptive language to describe a collection of gates and wires by what it enables us to do: perform arbitrary computations, hence a "computer". These were the same tasks that humans used to do before machines took over, so the collection of gates and wires is just an implementation detail.

Pattern matching, prediction, data generation, etc. are the tasks that modern AI systems allow us to do, yet you want us to refer to this as "intelligence" for some reason? That makes no sense to me. Maybe we need new higher level language to describe these systems, but "intelligence", "thinking", "reasoning" and "wit" shouldn't be part of it.

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

#242

Earlier quoted context omitted.

I think you'll need something like Meta's Large Concept Models to get past the language and token barrier.

I think you are right, even if I beleve next token prediction can work, I dont think it can happen in this autoregressive way where we fully collapse the token to feed it back in. Can you imagine how much is lost from each torch.multinomial? Maybe the way forward is in LCM or go JEPA, therwise, as this Apple paper suggests, we will just keep pushing the "pattern matching" further, maybe we get some sort of phase tran…

We have to ditch language processing. And we will with online energy based models that machines boot from.

Maxwell could not get the theory of electromagnetism to work until he ditched pulleys and levers he’d included to describe the mechanics.

We won’t get AGI until we realize “there is no spoon” and language has nothing to do with our intelligence, just with out social tribalism: https://www.scientificamerican.com/article/you-dont-need-wor...

Take language out of the equation and drawing a circle, triangles, letters is just statistical physics. We can capture in energy models stored in an online state, statistical physics relative to the machine; its electromagnetic geometry: https://iopscience.iop.org/article/10.1088/1742-6596/2987/1/...

Our language doesn’t exist without humans. It’s not an immutable property of physics. It’s obfuscation and mind viruses. It’s story mode.

The computer acting as a web server or an LLM has an inherent energy model to it. New models of those patterns will be refined to a statefulness that strips away unnecessary language constructs in the system; like a lot of software most don’t use just developers.

I look forward to continuing my work in the hardware world to further compress and reduce the useless state of past systems of though we copy paste around to serve developers, to reduce context to sort through, and improve model quality: https://arxiv.org/abs/2309.10668

Single function factory hardware with embedded “prompt” that will boot from a model and the machines state will scaffold itself from there are coming: https://creativestrategies.com/jensen-were-with-you-but-were...

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

#243

Reasoning models are just wrappers over the base model. It was pretty obvious it wasn’t actually reasoning but rather just refining the results using some kind of reasoning like heuristic. At least that’s what I assumed when they were released and you couldn’t modify the system prompt.

Not just wrappers. Some models are fine-tuned with reasoning traces.

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

#245
post #231

People are drawing erroneous conclusions from this. My read of this is that the paper demonstrates that given a particular model (and the problems examined with it) that giving more thought tokens does not help on problems above a certain complexity. It does not say anything about the capabilities of future, larger, models to handle more complex tasks. (NB: humans trend similarly) My concern is that people are extrap…

You can absolutely extrapolate the results, because what this shows is that even when "reasoning" these models are still fundamentally repeating in-sample patterns, and that they collapse when faced with novel reasoning tasks above a small complexity threshold. That is not a model-specific claim, it's a claim on the nature of LLMs. For your argument to be true would need to mean that there is a qualitative difference…

Human brains work the same way. Some of us are just better at analogy. I’ve worked with plenty of people who were unable to transfer knowledge of one area to another, identical area with different terms.

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

#246

Earlier quoted context omitted.

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?

If the reasoning model was truly reasoning while the flash model was not then by definition shouldn’t it be better at knowing when to use the tool than the non-reasoning model? Otherwise it’s not really “smarter” as claimed, which seems to line up perfectly with the paper’s conclusion.

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

#247
Figure 5 is really quite remarkable. It seems to show that normal LLMs are better at tasks where the correct answer is likely to be the next token. For tasks that require a small number of intermediate steps, current reasoning models do much better, but break down as the number of intermediate steps grow.

This seems to indicate that the next generation of models should focus on recursively solving small parts of the problem before function-calling another model to solve another small part of the problem and working it's answer into the reasoning loop.

Many seem to be citing this paper as an indication that LLMs are over - I think this indicates a clear path towards the next step function change in their abilities.

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

#248
post #44

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.

Yes it’s a net positive if you ignore the 100x extra power it took to actually run the reactor, so actually no it’s not net energy positive. Not even close.

Nothing to do with cost, we can not build a fusion reactor in 2025 with any amount of money that will produce more energy and goes into it.

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

#249

Earlier quoted context omitted.

Hypothetically. Imagine a simple, but humongous hash table, that maps every possible prompt of 20000 letters to the most appropriate answer in current time. (How? Let’s say outsourcing to east asia or aliens…) Would you say such mechanism is doing reasoning?

Let's outsource it to your mothers house instead.

Let’s do that. Still, this hash table, would be doing reasoning?

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

#250

Earlier quoted context omitted.

AI encompasses a wide range of algorithms and techniques; not just LLMs or neural nets. Also, it is worth pointing out that the definition of AI has changed drastically over the last few years and narrowed pretty significantly. If you’re viewing the definition from the 80–90’s, most of what we call "automation" today would have been considered AI.

Ah yes the mythical strawman definition of AI that you can never seem to pin down, was never rigorous, and never enjoyed wide expert acceptance. It's on par with "well many people used to say, or at least so I've been told, that ...".

That’s the point: AI is a marketing term and always has been. The underlying tech changes with every hype wave.

One of the first humanoid robots was an 18th century clockwork mechanism inside a porcelain doll that autonomously wrote out “Cogito Ergo Sum” in cursive with a pen. It was considered thought provoking at the time because it implied that some day machines could think.

BBC video posted to reddit 10 years ago: https://www.reddit.com/r/history/s/d6xTeqfKCv

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