[antics]

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 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.

[imiric]

> 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 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.

[BoiledCabbage]

> 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 levels of abstraction.

Clearly an LLM is not "omniscient". It doesn't require a post to refute that, OP obviously doesn't mean that literally. It's an analogy describing two semi (fairly?) independent axes. One on breadth of knowledge, one on something more similar to intelligence and being able to "reason" from smaller components of knowledge. The opposite of which is dim witted.

So at one extreme you'd have something completely unable to generalize or synthesize new results. Only able to correctly respond if it identically matches prior things it has seen, but has seen and stored a ton. At the other extreme would be something that only knows a very smal set of general facts and concepts but is extremely good at reasoning from first principles on the fly. Both could "score" the same on an evaluation, but have very different projections for future growth.

It's a great analogy and way to think about the problem. And it me multiple paragraphs to write ehat OP expressed in two sentences via a great analogy.

LLMs are a blend of the two skills, apparently leaning more towards the former but not completely.

> What we do have are very good pattern matchers and probabilistic data generators

This an unhelpful description. And object is more than the sum of its parts. And higher levels behaviors emerge. 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.

[tim333]

>There's nothing "omniscient" or "dim-witted" about these tools

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?

[antics]

I am not sure we are on the same page that the point of my response is that this paper is not enough to prevent exactly the argument you just made.

In any event, if you want to take umbrage with this paper, I think we will need to back up a bit. The authors use a mostly-standardized definition of "reasoning", which is widely-accepted enough to support not just one, but several of their papers, in some of the best CS conferences in the world. I actually think you are right that it is reasonable to question this definition (and some people do), but I think it's going to be really hard for you to start that discussion here without (1) saying what your definition specifically is, and (2) justifying why its better than theirs. Or at the very least, borrowing one from a well-known critique like, e.g., Gebru's, Bender's, etc.

[Kon5ole]

>They have no wit. They do not think or reason.

Computers can't think and submarines can't swim.

[drodgers]

> 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 overhang. ie. we're only one or two breakthroughs away from something which is both kinda omniscient (where we are today), and able to out-think you very quickly (if only through dint of applying parallelism to actually competent outcome-modelling and strategic decision making).

That combination is terrifying. I don't think enough people have really imagined what it would mean for an AI to be able to out-strategise humans in the same way that they can now — say — out-poetry humans (by being both decent in terms of quality and super fast). It's like when you're speaking to someone way smarter than you and you realise that they're 6 steps ahead, and actively shaping your thought process to guide you where they want you to end up. At scale. For everything.

This exact thing (better reasoning + agency) is also the top priority for all of the frontier researchers right now (because it's super useful), so I think a breakthrough might not be far away.

Another way to phrase it: I think today's LLMs are about as good at snap judgements in most areas as the best humans (probably much better at everything that rhymes with inferring vibes from text), but they kinda suck at:

1. Reasoning/strategising step-by-step for very long periods

2. Snap judgements about reasoning or taking strategic actions (in the way that expert strategic humans don't actually need to think through their actions step-by-step very often - they've built intuition which gets them straight to the best answer 90% of the time)

Getting good at the long range thinking might require more substantial architectural changes (eg. some sort of separate 'system 2' reasoning architecture to complement the already pretty great 'system 1' transformer models we have). OTOH, it might just require better training data and algorithms so that the models develop good enough strategic taste and agentic intuitions to get to a near-optimal solution quickly before they fall off a long-range reasoning performance cliff.

Of course, maybe the problem is really hard and there's no easy breakthrough (or it requires 100,000x more computing power than we have access to right now). There's no certainty to be found, but a scary breakthrough definitely seems possible to me.

[sitkack]

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.

[sitkack]

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

[antics]

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.