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 continue to think that the models are in fact getting less dim-witted, while the AI skeptics will continue to think these apparent gains are in fact entirely a biproduct of "increasing" "omniscience." The razor will have to be a lot sharper before people start moving between these groups.
But, anyway, it's still an important question to ask, because omniscient-yet-dim-witted models terminate at "superhumanly assistive" rather than "Artificial Superintelligence", which in turn economically means "another bite at the SaaS apple" instead of "phase shift in the economy." So I hope the authors will eventually succeed.
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 generate the final answer. I.e. they do real-time tuning based on synthetic data.
This is a neat trick, but it doesn't solve the underlying problems that plague these models like hallucination. If the "reasoning" process contains garbage, gets stuck in loops, etc., the final answer will also be garbage. I've seen sessions where the model approximates the correct answer in the first "reasoning" step, but then sabotages it with senseless "But wait!" follow-up steps. The final answer ends up being a mangled mess of all the garbage it generated in the "reasoning" phase.
The only reason we keep anthropomorphizing these tools is because it makes us feel good. It's wishful thinking that markets well, gets investors buzzing, and grows the hype further. In reality, we're as close to artificial intelligence as we were a decade ago. What we do have are very good pattern matchers and probabilistic data generators that can leverage the enormous amount of compute we can throw at the problem. Which isn't to say that this can't be very useful, but ascribing human qualities to it only muddies the discussion.
I disagree in that that seems quite a good way of describing them. All language is a bit inexact.
Also I don't buy we are no closer to AI than ten years ago - there seem lots going on. Just because LLMs are limited doesn't mean we can't find or add other algorithms - I mean look at alphaevolve for example https://www.technologyreview.com/2025/05/14/1116438/google-d...
>found a faster way to solve matrix multiplications—a fundamental problem in computer science—beating a record that had stood for more than 50 years
I figure it's hard to argue that that is not at least somewhat intelligent?
The fact that this technology can be very useful doesn't imply that it's intelligent. My argument is about the language used to describe it, not about its abilities.
The breakthroughs we've had is because there is a lot of utility from finding patterns in data which humans aren't very good at. Many of our problems can be boiled down to this task. So when we have vast amounts of data and compute at our disposal, we can be easily impressed by results that seem impossible for humans.
But this is not intelligence. The machine has no semantic understanding of what the data represents. The algorithm is optimized for generating specific permutations of tokens that match something it previously saw and was rewarded for. Again, very useful, but there's no thinking or reasoning there. The model doesn't have an understanding of why the wolf can't be close to the goat, or how a cabbage tastes. It's trained on enough data and algorithmic tricks that its responses can fool us into thinking it does, but this is just an illusion of intelligence. This is why we need to constantly feed it more tricks so that it doesn't fumble with basic questions like how many "R"s are in "strawberry", or that it doesn't generate racially diverse but historically inaccurate images.
When you consider how humans and other animals learn, knowledge is carried over. I.e. if we learn how to solve a maze on paper, we can carry this knowledge over to solve a hedge maze. It's a contrived example, but you get the idea. When we learn, we build out a web of ideas in our minds which we can later use while thinking to solve other types of problems, or the same problems in different ways. This is a sign of intelligence that modern AI systems simply don't have. They're showing an illusion of intelligence, which as I've said before, can still be very useful.
LLMs are leagues ahead of viruses or proteins or water. If you put an LLM into a code editor with access to error messages, it can solve a problem you create for it, much like water flowing through a maze. Does it learn or change? No, everything is already there in the structure of the LLM. Does it have agency? No, it’s a transparently deterministic mapping from input to output. Can it demonstrate intelligent behavior? Yes.
This is why I think it's important that if we're going to call these tools intelligent, then they must follow the processes that humans do to showcase that intelligence. Scoring high on a benchmark is not a good indicator of this, in the same way that a human scoring high on a test isn't. It's just one convenient way we have of judging this, and a very flawed one at that.
Anyway, cheers for the discussion!