8 ms·
Position: LLMs Can't Jump
- jvanderbot 2mo agoCame for: "A computer once beat me at chess, but it was no match for me at kick boxing." TFA was actually about leaps of intuition, sadly. One of the experiments I've heard proposed around here is to somehow create an LLM from all text up to 1980 or 1990 and see if it can get back to making itself.
- Normal_gaussian 2mo agoThe curious case here is how much of a description do we give it of itself? That would almost certainly dominate success rates. My feeling is that a prompt would have to provide a vague description of a program that meaningfully passes something like a Turing test, an API to conform to, an expectation of novel construction (no 'ifs all the way down'), and then a requirement to search broadly and pursue promising ideas and not get hung up on the philosophy. Anything more precise feels like it would corrupt the test, but as it is that description feels doomed to loop before even trying the interesting parts.
- ModernMech 2mo agoI wonder if we could just tell it to invent itself without any description and see if it can I introspect enough through its own interface to figure out what it is.
- elar_verole 2mo agoI think this can't work because an LLM needs too much data, and before the internet there probably just wasn't enough to get close to what we have now
- jvanderbot 2mo agoEven simpler: Can GPT-2 anticipate and build Gwen/Deepseek? I think the answer is almost trivially "no", so I wonder what changed?
- ben_w 2mo agoLots of things changed, GPT-2 is small (1.5e9) and is also a base model, so it is only doing next-token/autocomplete rather than prompt-response like even the first ChatGPT-3.5 was doing.
- jebarker 2mo agoJust for the sake of clarity: all LLMs up to today are still only doing next-token/autocomplete. The training process got additional stages to shape the model weights, but standalone LLMs are still deployed essentially identically.
- ben_w 2mo agoIf you gave GPT-2 a question and ended with a "?", it might answer, but also it might write several more questions in a similar category. IMO, the mechanism isn't the important thing, the behaviour is. If you look at the step-by-step, we are also looking for the next word or motor action (and for whoever is about to suggest that we humans plan ahead, Transformer-based LLMs have been shown to also do this); as this is not a useful description of what it means to be a living brain, I'd say it's also not a useful description of what makes everything post-InstructGPT different from what came before.
- jebarker 2mo agoI agree completely - behaviorally the models have changed drastically due to RLHF, RLVR and now maybe even more so due to agentic harnesses. But the mechanism of prediction hasn’t changed, that was all I was clarifying.
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- hackernudes 2mo agoMaybe we can synthesize large amounts of limited information. I thought that new training data is mostly synthetic anyway.
- inigyou 2mo agoWhy couldn't an LLM, if it was smart enough, generate and consume its own data? I know the answer: because it leads to model collapse. But why is that? Wouldn't a smart model not collapse? It's seeming like they keep getting smarter because we keep pouring more of our own knowledge into them, not because they are actually getting smarter. And yes, sometimes a dumb but persistent bruteforcer can make new discoveries.
- Garlef 2mo ago> if it was smart enough and i think this is exactly the crux; the really big models need really big datasets and current gen LLMs get a lot of training data beyond "all books + all of the internet" the objection is then that producing this additional data would already confound it with pre "virtual cutoff date" knowledge (since the training data probably implies mathematical and SWE concepts that were developed post "virtual cutoff date")
- smusamashah 2mo agoIf it is smart enough to generate data it can consume to train itself better, it is already smart enough to not need to do that.
- inigyou 2mo agoIf a human is smart enough to do the Michelson-Morley experiment, they are smart enough to not need to do that.
- Plasmoid 2mo agoIt's because LLMs are entropy generators. That's not a bad thing for what people are doing. But to prevent model collapse you need a way to pump down the entropy. Much like in thermo, it's an expensive and slow process.
- Kinrany 2mo agoThey are already trained on generated data I believe
- naasking 2mo agoTypical LLM pretraining is very inefficient with data. NanoGPT slowrun shows that data can be used much more efficiently.
- throwaway314155 2mo agoArticle was plenty interesting to me.
- Tade0 2mo agoIs that how chessboxing was invented? Genuinely asking.
- the_af 2mo agoNope. Chessboxing was the invention of comics book artist Enki Bilal (and he's credited with this in Wikipedia). I first saw it in his Nikopol trilogy. Because life is weird, it then became a real thing. It's unrelated to computers playing chess. It predates Kasparov's first defeat by Deep Blue. I don't remember any mention of computers being good at chess in the trilogy, either. Or any computers, for that matter.
- ambicapter 2mo agoFunny, I knew about chessboxing and Enki Bilal, but had no idea one birthed the other.
- ben_w 2mo ago> One of the experiments I've heard proposed around here is to somehow create an LLM from all text up to 1980 or 1990 and see if it can get back to making itself. Could be, but preventing leakage from more modern stuff can be challenging. This was attempted with Victorian public domain content: https://www.estragon.news/mr-chatterbox-or-the-modern-prometheus/ https://www.estragon.news/mr-chatterbox-or-the-modern-promet... I can't find the citation right now, but I think people found it was leaking anachronisms? So this probably wasn't as well filtered as the creator had hoped?
- morkalork 2mo agoProgress followed improvements in hardware, would you have to give access to modern hardware in the experiment for it to use? How much could it infer from it?
- ben_w 2mo ago> would you have to give access to modern hardware in the experiment for it to use? At a minimum, yes. IIRC, the sum total of all compute manufactured over history only reached the minimum needed to train an OK LMM in the mid 00s. > How much could it infer from it? Only way to find out is to try.
- bee_rider 2mo ago> One of the experiments I've heard proposed around here is to somehow create an LLM from all text up to 1980 or 1990 and see if it can get back to making itself. This is an interesting experiment but I wonder if it would be possible to prevent some sort of retrospective bias. For example, I’d expect the experiments that lead to relativity to be over-represented in our catalogue of scientific literature prior to 1900, just because in retrospect they were important, so the records about them were preserved.It would have to be a very intentionally constructed corpus, I think.
- unfitted2545 2mo agoIf would be interesting to see 5 billion LLM's working together, each with random mutations (temperature ig). Would we essentially be looking at a society through a petri dish? Ofc 5 billion is quite a lot of compute.
- handoflixue 2mo agoThey did that with "Talkie", a model trained on 1930 and before. It had the ability to assemble crude Python programs, but there were definitely some leaks in the training data (it knew about stuff like World War 2) so not 100% perfect. Still seems like a pretty reasonable "proof of concept" https://didof.dev/blog/talkie-1930-llm-reasoning/ https://didof.dev/blog/talkie-1930-llm-reasoning/ seems like a decent overview
- nativeit 2mo agoPrevious: https://news.ycombinator.com/item?id=49162791 https://news.ycombinator.com/item?id=49162791
- nativeit 2mo agoAlso https://news.ycombinator.com/item?id=49136070 https://news.ycombinator.com/item?id=49136070
- HelloUsername 2mo agoYou posted this twice now. Why not just edit your original comment?
- nativeit 2mo agoI don’t need to edit my original comment, it doesn’t contain any mistakes. All six of the links across three comments are each distinct, separate posts going to either the same article, or others with identical headlines.
- HelloUsername 2mo agoAh sorry, I meant this specific link: https://news.ycombinator.com/item?id=49136070 https://news.ycombinator.com/item?id=49136070, you posted it twice. And I also meant it could've been one post, to avoid nesting.
- nativeit 2mo agoAlso: https://news.ycombinator.com/item?id=49136070 https://news.ycombinator.com/item?id=49136070 https://news.ycombinator.com/item?id=49096837 https://news.ycombinator.com/item?id=49096837 https://news.ycombinator.com/item?id=46890333 https://news.ycombinator.com/item?id=46890333 https://news.ycombinator.com/item?id=46870562 https://news.ycombinator.com/item?id=46870562 All of these are titled “LLMs Can’t Jump”
- petsku 2mo ago[flagged]
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- conartist6 2mo agoThis tracks for me as someone making keeps of intuition in little-explored areas. I just don't see any of the LLM users around at all. Clearly some force is guiding them all away from thinking any of the "leap of faith" thoughts that I am thinking.
- sobiolite 2mo agoThe theory is that creative leaps in theoretical physics require a grounding in sensory experience, but the obvious counter-argument is that humans can make creative leaps in abstract fields without such sensory grounding. They do address this at the end, saying "In abstract domains such as Mathematics or Computer Science, the Sense Experience (E) may be grounded in high-dimensional topology or have other goals such as generality or minimality." But if such sense experience is possible in abstract domains via some high-dimensional topology, why could a sufficiently advanced LLM not develop an equivalent high-dimensional topology for domains like physics and use it to make creative leaps?
- Spacecosmonaut 2mo agoIsnt the sensory grounding even in abstract cases some (limited) intuition that simulates in a mental world model?
- bob001 2mo agoThat's an interesting analogy. My gut sense is that theoretical mathematics requires a high level of intelligence versus more grounded domains. That may imply that deficiency in grounding can be made up for with intelligence and basically reverse engineering the gaps in grounding from first principles/limited grounding. The ultimate question would then be what is the tradeoffs between grounding and raw intelligence for the same outcome.
- roenxi 2mo ago> ...but the obvious counter-argument is that humans can make creative leaps in abstract fields without such sensory grounding... But we have no idea at how good humans are at that. Given the appalling failures of humans to handle even basic statistical situations like identifying that the same thing happens over and over, it might be that they are hilariously bad at creative leaps in abstract fields, it is just we have had nothing better available to measure against. We've spent about as long as decision theory existed trying to convince people to use it instead of flailing. Limited success, usually in exceptional cases. And the paper seems a bit dodgy, we have models created with sensory data available. No reason a LLM can't be trained on more sensory data than a human can accumulate in one lifetime. There is a lot of visual data on YouTube.
- Mistletoe 2mo agoI feel like you could just add some noise or randomness to the LLM and start approximating the leaps that the human mind uses to solve and understand unrelated things. Maybe that’s naive, it’s just coming from my organic computer in my skull.
- Der_Einzige 2mo agoHahaha you just derived temperature from first principles. Turns out temperature is pretty bad too, you can find ways to sample from deeper in the distribution without distorting it. Great example is XTC (exclude top choices), In a few weeks/months it'll also have a proper scholarly paper with peer review.
- dooglius 2mo agoTechnically true, but the counter argument would be that the probability of this working would be ~ 2^(-(entropy_of_leap)) for an LLM (presumably intractable) and a human would succeed at a higher probability.
- Mistletoe 2mo agoIf we could just get the LLMs to take showers and dream, we would get some novel thoughts coming.
- kfarr 2mo agoBest comment on this from 6 months ago: https://news.ycombinator.com/item?id=46870575 https://news.ycombinator.com/item?id=46870575
- yorwba 2mo ago"Best" because of the irony that it was written by Claude? https://news.ycombinator.com/item?id=46901199 https://news.ycombinator.com/item?id=46901199
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- GodelNumbering 2mo agoI have been writing a 'paper' [1] on an adjacent topic for months now. At some point, I decided to make it an empirical paper vs position paper. I am still chasing the experiments (when I get some free time waiting for agentic loops) For this paper specifically, after reading the abstract [2], I felt almost certain that the author would have used Judea Pearl's ladder of causation (https://web.cs.ucla.edu/~kaoru/3-layer-causal-hierarchy.pdf https://web.cs.ucla.edu/~kaoru/3-layer-causal-hierarchy.pdf) but they did not. Would have probably been a better argument to make. [1] paper in quotes because it may never get published (it is over 20 pages atm). the core argument is that lack of native adjacency resolution makes problems harder and sample inefficient, not impossible [2] "Using Einstein’s formulation of General Relativity as a case study, we demonstrate that LLMs are structurally incapable of creating new foundational axioms, particularly when observational data is scarce. " Also, the claim that 'LLMs are structurally incapable of creating new foundational axioms' is provably false depending on where you place 'fundamental'.
- bob1029 2mo agoI think this is more of a function of the harness and the environment than the LLM. I've seen some LLM interactions over complex environments like Godot and Unity that challenges the notion that there is no "jumping" going on at all. An LLM in isolation from its environment might as well be a brain in a vat in some dark cave. You need an external environment to sample from and act upon to make forward progress.
- altmanaltman 2mo agoWhy does the cave need to be dark if its just a brain in a vat?
- mayhemducks 2mo ago'Cause the brain is in a glass vat. and brains have eyes too. https://www.youtube.com/watch?v=pc3uWxs9adw https://www.youtube.com/watch?v=pc3uWxs9adw
- doginasuit 2mo agoI'm also amazed at the degree an LLM can get the drift with a vague or incomplete prompt. The ability to perceive and operate based on patterns that go beyond the language in the text makes it seem like they would be unusually good at taking leaps that haven't occurred to us.
- defgeneric 2mo agoWorth reposting a follow-up tweet from the author Tom Zahavy [1] after this made the rounds on X/Twitter recently: > A few reflections on my "LLMs Can’t Jump" paper: > My position paper recently got some traction here, so I wanted to share a few thoughts and clarify a few things. > First things first: some people are framing this as "DeepMind is throwing cold water on AI for science" or claiming the paper argues LLMs can never make real scientific discoveries. This is NOT the case. > This is a personal position paper, not the company's view on AI for science. This is also not my position. As a core contributor to AlphaProof (the first AI system to win an IMO medal), I know firsthand that my colleagues at DeepMind, other frontier labs, and academia have made amazing discoveries with LLMs and will continue to do so. This paper is NOT an "LLMs are a dead end" kind of thing. > Rather, the paper is the result of a deep dive I took to study the invention of General Relativity. I wanted to explore what it would take for a modern AI system to make that exact kind of jump. Specifically, I focused on the equivalence principle—a key axiom that Einstein formulated through thought experiments grounded in his physical intuition. I was trying to figure out what it would take to give modern AI systems that sort of thinking. > Giving AI this specific capability isn't necessarily the most urgent thing to do next. It is very likely that improving our current recipes will lead to many exciting discoveries in the near future. In fact, that is what I am personally working on these days (sorry to disappoint you!). It is also quite possible that I am wrong, and that simply scaling our current systems will lead to new inventions in physics and elsewhere. > Nevertheless, this was my position last winter when I wrote the paper, and I'm sticking to it. I think that there are a few interesting ideas to explore in this space which could influence the next generation of AI systems. I was very lucky to receive a lot of interesting feedback about this position—thank you for all the messages! [1] https://x.com/TZahavy/status/2082401499628376180 https://x.com/TZahavy/status/2082401499628376180
- wildfireday2 2mo agoMoreover anyone glomming onto this paper for goal-post-shifting “AI can never” should: 1. Read the last sentence of the abstract, and 2. Reflect that frontier reasoning agents already increasingly integrate multimodal models.
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- reliablereason 2mo agoClearly LLMs cant do leaps of intuition since their "intuition" is locked after training ends. The only way a LLM can come up with new ideas if the "idea" appeared as a generalisation durring training or if it was achieved using reason in chain of thought.
- buzzin__ 2mo agoOr some randomness is aomehowntroduced in the output, which happens after every word, unless you set the temperature to zero.
- reliablereason 2mo agoThat would not be intuition that is just randomness. Intuition is not randomness. A jump in intuition comes from automatic processes reorganising the relational structure of conceptual models. There is no reorganisation of the model durring inference.
- tpolm 2mo ago> There is no reorganisation of the model durring inference. one could argue that model can reorganize / interact with prior knowledge captured in text form (edit files) hence there can be reorganisation
- reliablereason 2mo agoThat would "using reason in chain of thought".
- Enginerrrd 2mo agoI think this is a bad way to look at it. LLMs can probably conceive of most things that are representable within the embedding space. Ordinarily in mathematics there’s a TON of papers to write just combining low level problems with different techniques. Better still, and often considered groundbreaking is borrowing techniques from other fields and adapting them or creating analogous methods to solve problems. A lot of landmark papers have been written this way. This is also what transformers are sort of good at within other contexts. They have super human breadth so I’m hopeful they’ll become real assets in math for a long time. Though the leaps necessary to adapt a technique in a nonobvious way might be too much for a while longer. We’ll see. Truly novel techniques are quite rate indeed and I don’t know if LLMs can represent them faithfully in their embedding space or not. My inclination is that they probably can most of the time, but I don’t know. Mathematicians would describe such thins as “alien”.
- quantum_mcts 2mo agoThe popular retelling of how Einstein created Special Relativity to "Resolve the contradictions of Michelson-Morly experiments" is very reductive to the history of the question. The epitome is the quote from the paper: > From the two postulates, Einstein derived the Lorentz trans- formation ... If Einstein derived them, who is "Lorentz"? The groundwork for Special Relativity was the study of electrodynamics and symmetries of Maxwell equations. The Einsteins paper was literally called "On the Electrodynamics of Moving Bodies" and never cites Michelson and Morley.
- eru 2mo agoI agree with you in general. However: > If Einstein derived them, who is "Lorentz"? You can (re-) derive a lot of existing stuff.
- kergonath 2mo ago> You can (re-) derive a lot of existing stuff. Einstein was aware of Lorentz and the transform. He was aware of Poincaré as well. He knew the state of the art for his time.
- eru 2mo agoRe-deriving the start of the art cleanly with fewer postulates is good!
- oliculipolicula 2mo agoAnd so I'm skeptical of TFA's claim that "creativity _isn't [just]_ compression" Imho there's also a clean argument against the existence of LLM-understanding: chatbots have been unable to summarize to experts (see my reply to you in the other thread) their own findings. Even after prolonged interrogation. They were unable to _compress_ their own findings. Thus they might not actually understand what they have actually done. (They might barely pass an oral thesis defense) (One may object-- that proofs aren't data that can be "compressed". But then doesn't the process of abduction generalise the very idea of data? to.. ?)
- rsfern 2mo agoI found this paper really thought provoking, but I think the conclusion of “world models are the solution” leaves something to be desired. People are already equipping agentic systems with physical simulation tools and exploring action-conditioned world models. This is cool because you can change the rules of the simulation and observe what happens, but it doesn’t address the core question of what to change the rules to, or even what the goal should be in the first place.
- brainless 2mo agoI have a weird thought experiment: If you give a GPT-2/3 level LLM tools to search the internet - any document, can it build bigger, better LLMs? You may think this is not a good test because an older (or say a smaller) LLM can study from the knowledge on the Internet and build. But we are like that - we can access the Universe through our senses. Can we ever produce anything that is beyond this Universe? I think an LLM that is lacking in knowledge can build more complex systems as long as it can access more data.
- ACCount37 2mo agoIt's a very shaky position, and the empirical track record of "LLMs can't..." is in itself a reason to call it into doubt. Every "can't" of this nature was followed by a discovery of "they can, just poorly", and then by that "poorly" improving steadily generation to generation. The paper doesn't provide a way to measure or quantify this elusive "jumping" capability, not even as an approximation. It just throws "can't jump" out there, as if "abduction" is an established class of problem with known computational properties and requirements that the LLM architecture fails to satisfy. It's none of those things - and the paper makes the claim without backing it by anything but rhetoric attempts at persuasion. The proposed solution is also dubious. The empirical track record of dedicated "world models" for reasoning and problem-solving is, frankly, downright abysmal. Even integrating multimodal data into LLMs has failed to yield general reasoning capability gains. LeCun's misadventures in the field aside, the main frontier lab that pushes in favor of "improving reasoning via multimodal fusion" is GDM - and Gemini isn't exactly a paragon of frontier reasoning capabilities. It has strong multimodal capabilities, but lags behind both OpenAI and Anthropic in performance outside that - while Anthropic is the lab that always treated multimodal grounding as an afterthought, and still trades blows with OpenAI at the very edge of the performance frontier. Multimodal grounding seems to work great as a way to improve an AI's ability to deal with those specific modalities, but it falters outside that. Now, it's not impossible that everyone who tried multimodal world models for reasoning is just doing it wrong, and there is an undiscovered recipe for multimodal grounding that results in a step change in AI capabilities. But the results we have so far suggest it to be unlikely.
- petesergeant 2mo ago> the empirical track record of "LLMs can't..." is in itself a reason to call it into doubt ... Every "can't" of this nature was followed by a discovery of "they can, just poorly", and then etc What LLMs are "fundamentally incapable" of doing has striking parallels to https://en.wikipedia.org/wiki/God_of_the_gaps https://en.wikipedia.org/wiki/God_of_the_gaps
- vatsachak 2mo agoOkay here's something LLMs can't. They can't solve problems that are longer than ~10 pages of math. They also can't maintain codebases without supervision. It's because they are have no memory and use various tricks to supplant that fact.
- pama 2mo agoThe early physics background is messy and incorrect. I didnt read the full position paper, but from its start: The Lorentz transformations were by Lorentz, well before Einstein’s paper on special relativity; the principle of relativity also existed before the Einstein paper. The math was all there, with steps taken by Maxwell, Voigt, Larmor, Lorentz, and Poincare. Einstein supplied a clean physical interpretation, making all inertial frames equivalent, making simultaneity frame dependent, and explaining length and time deformations without the need of the concept of ether. Skimming the end of the paper with the arguments about lack of abduction or inability to make the analogy without sensory experience, I see that this paper is unfounded speculation rather than solid/hard philosophical logic. As a position paper it is OK to appear, but i think it misses the point of how LLMs or other autoregressive learners of future states can build analogies and intuition that can help them formulate new theories of the world. Soon it will be more obvious to everyone, so I am not very worried about these writings.
- skew-aberration 2mo agoYes, I made a similar comment on the other mention today. The author seems to misunderstand what GR is / what it added to physics too (creating self-referential field equations to handle mass/energy equivalence - linear field equations without instantaneous 'action-at-a-distance' existed much earlier). The notion that it was a 'small signal' is totally false. Once you've hypothesized that the apparent mass of objects depends on the observer, you need to show that your theory gives consistent results for trajectories of objects in gravitational fields. https://news.ycombinator.com/item?id=49177965 https://news.ycombinator.com/item?id=49177965
- juleiie 2mo ago[flagged]
- prabhanjana_c 2mo agoCalculator for language, is a nice insight. I visualize generally as LLM's as big mathematical expression that produce next word. But then , one difference I found on LLM's is on scaling laws, where at some point, it have interesting emerging properties, that nobody thought would be possible now being possible. Tools: Though It is a tool, but powerfull tool that is automating existing manually done jobs, large scale. Adapting to new roles where we definite goals, needs and motives judging of AI output, at large level is an issue. And humans are used to day to day repeating job, doing same thing repeatedly. Now the AI is taking over that. I see that is a challenge. Also I see we are moving to creative world, where we will spend more time on creating something really new, leaving mechanized parts to AI.
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- zie1ony 2mo agoInterestingly, halucinations might be the way to achieve that.
- Yopolo 2mo agoWe just put different things together and then we evaluate it. In math its simple: does the verification say its okay. If its mechanical: is any property better than what we have already. etc.
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- redwood 2mo agoFor those who may not be aware this is a clever title derived from a film title https://en.wikipedia.org/wiki/White_Men_Can%27t_Jump https://en.wikipedia.org/wiki/White_Men_Can%27t_Jump
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- yomismoaqui 2mo agoWhy everybody is obsessed with replacing humans with LLMs when it seems like the most profitable use cases (like coding agents) rely on enhancing human capabilities? Until LLMs have some 0% error humans will have to be in the loop (even if they only serve to take responsibility of the process).
- daun_gee 2mo ago[dead]
- StevenWaterman 2mo ago> The only way to justify trillion dollar valuations Also possible if you make god
- daun_gee 2mo ago[dead]
- goatlover 2mo agoWould have been better if AI was known as Augmented Intelligence as it's a tool to to boost our own intelligence. Instead the term that promises science fiction futures predominated, and now it's being used to raise a ton of money. Saying you want to make workers more productive and provide better tools for people just isn't that sexy.
- dtj1123 2mo agoNeither can I, if I'm being honest.
- setnone 2mo agoLLMs don't have legs yet. If you're smart but can't touch things you only keep being smart
- ACCount37 2mo ago[dead]
- kdavis 2mo agoThe article seems like an interesting Gedankenexperiment. However, I think it overrotates on the GR analogy. For example "..ARC captures the logical leap, it misses the manipulative component—the physical sensation and embodied simulation..." makes lots of assumptions on how such a discovery must occur, e.g. through "physical sensation and embodied simulation". Results matter, not the path there. For example, quantization of energy, at the core of QM, wasn't discovered through "physical sensation and embodied simulation" at all. Planck simply found that if energy is quantized, then one obtained the observed black-body radiation spectrum. There was no "physical sensation and embodied simulation".
- m3kw9 2mo agoEven myself, I really can't remember a time where I had this "jump". Is very subjective to feel this jump
- m3kw9 2mo agoIs this a reference to the movie "White man can't jump"? If so they are in a surprise because the movie says otherwise.
- m3kw9 2mo agoThe proof is in the pudding, so far there isn't a proven (E=mc^2 type) breakthrough LLM's had made yet.
- _superposition_ 2mo agoWhat I find absolutely fascinating about this paper is that recently I made the leap that physical representation was a necessary ingredient for invention based on my own experience (lack of abundance of evidence) So I intuitively agree with the premise. It's kinda meta.
- inerte 2mo agoMaybe LLMs can't, but another form of AI will. I hope nobody is interpreting this as "nothing will never be as good as us". I see similar thinking in stories of how humanity got here. Religion has thousands of years adapting to this problem, every time we explain something, the goal post moves. Catholics today accept evolution (or least the church does), but it is the "jump" from monkeys to humans where God is the only explanation. Just 5 years ago we didn't have a technology that knows more about everything than even most experts. We keep coming up with benchmark after benchmark and LLM/AI keeps destroying them. Now we've moved the benchmark to "the jump". Again, maybe it's LLMs or the way we currently do them that can't do this, but eventually something will.
- kskdkwkdjw 2mo ago> Just 5 years ago we didn't have a technology that knows more about everything than even most experts. And we still don’t. What we have are simply very advanced search results aggregators with delusions of personality. Just because your fridge says “I” doesn’t mean it is a person.
- bigstrat2003 2mo ago> Just 5 years ago we didn't have a technology that knows more about everything than even most experts. We still don't have that. LLMs have shown time and time again that they don't know a single thing and are incapable of reasoning.
- naasking 2mo agoNo such thing has been shown.
- yk 2mo agoThe paper is from the 30th of April this year, openAi announced the counter example to the unit distance problem on the 20th of May. That is to say this paper seems to have aged not much but quite poorly.
- Hammershaft 2mo agoI'm skeptical a counterexample is good evidence of creative intuition.
- wnmurphy 2mo agoI had a related insight, but in the domain of humor [1]. LLMs are inherently probabilistic, and there's currently no mechanism for producing an orthogonal directional change in the path traced through a latent space which is also contextually relevant (landing on a punch line). In other words, LLMs are fundamentally incapable of making intuitive/orthogonal leaps in context. It might be possible to add this capability with a new architectural component like transformers, but specifically for making "left turns"/intuitive leaps. [1] https://wnmurphy.com/llms-cant-do-humor/ https://wnmurphy.com/llms-cant-do-humor/
- ACCount37 2mo agoYou have committed the classic blunder of confusing your abstraction layers. "Probabilistic next word prediction" and "humor" sit about as far apart as "modulating airflow with meat flaps" and "humor" do. One is an interface through which an action is performed and the other is a highly abstract capability. Would you claim that a podcast comedian is fundamentally incapable of being funny because all he ever does is wiggle the air with his throat meat flaps? Probably not. Absolutely nothing about "probabilistic next word prediction" forbids "making intuitive/orthogonal leaps in context". The interface is expressive enough. And empirically? The "sense of humor" in LLMs is yet another "a function of model scale" capability. GPT-4.5 was reportedly funnier than both GPT-4o and o1. Fable 5 is reportedly funnier than Opus 4.x. It's one of those ever-elusive "big model smell" signs that are hard to measure with anything other than vibes. Under the "humor as an opposed social intelligence test" family of hypothesis, what "being funny" reflects is the funny guy's ability to model and predict you and your reactions. For the comedian to be able to make the audience laugh, he must know his audience well, model it accurately enough to be able to spot the "breaking points" of humor, things they'd find unexpected and clever and thus "funny", and then weave those things into the jokes. Then, a bigger LLM gets better at humor because it has a more accurate model of how humans think of things - including the "ha-ha" gaps. It's a "theory of mind" capability. It's not "special", it's just hard.
- wnmurphy 2mo agoHumor requires a sudden orthogonal leap from context. That's what a punchline is. I think you're saying that you can eventually train models to arrive at that destination by training on existing jokes, effectively encoding these leaps as probabilities. In that case, the model isn't actually making an intuitive/comedic leap; they're just following new probability chains in attempting to approximate examples they've seen in training. I'm suggesting that something architecturally different is necessary to create a model which can make intuitive/comedic leaps. Try to get a frontier model to write a clever, funny joke which hasn't been seen before. Or, try to get it to make an intuitive leap that leads to a novel discovery. You can use them to guide your own efforts along these lines, as a sounding board. But with current architecture I just don't think either is possible for an LLM to do on its own.
- funflusion 2mo ago[dead]
- zamalek 2mo agoMaybe modulating temperature can help here: have the LLM come up with ideas at high temperature, and then critique them at low. This is also tied to halucinations: it is something that humans do (for writing fiction, and for "jumps") - but what LLMs currently lack is intellectual honesty. Coming up with bullshit is fine (and in this context valuable) - the important bit is putting those ideas through some form of rigor, or just immediately turn around and admit to talking shit. So I'd arge that hallucinations are what prevent LLMs from doing this in a useful way.
- km3r 2mo agoI wonder if giving the models context of the temperature of its past generations would help here. Like a thinking mode that deliberately has a section that is high temperature, while the rest is lower.
- zamalek 2mo agoI think you'd need to insert "critique these ideas." That's where the intellectual honesty comes in - I suspect that _all_ current LLMs would continue on with and truly stupid ideas as though they were gospel, their profound incompetence at backtracking on what they have said is something the industry hasn't figured out. If we could make progress in that area, maybe CoT could gradually decrease as it approaches its limit, or maybe the LLM could control the temperature of the next token itself (how this would be trained, I have no idea).
- heaney-555 2mo agoSo the goalposts have moved all the way to "LLMs can't do what Albert Einstein did"?
- killerstorm 2mo agoThis is literally an opinion of one dude which is not backed by any kind of quantitative evidence. It's actually possible to answer this question rigorously: 1. Define a scientific result which qualifies as a "jump". They should be frequent enough that they happen every year - otherwise one might say humans can't jump either. 2. Identify all such "jumps" in articles published in 2026, and use LLM with 2025 knowledge cut-off to re-derive these results with minimal amount of information. It really irks me that people boost these low-effort articles just because they confirm pre-conceived notion that LLMs are limited
- andai 2mo agoI boost it so someone else will see it and prove it wrong.
- emp17344 2mo agoLLMs most definitely are limited. What’s your position, that LLMs have no limits? That’s obviously wrong, and the fact you hold such an unreasonable position may be why you react so strongly to pieces like this one.
- killerstorm 2mo agoWhat's the obvious limitation? To Yann LeCun it was "obvious" that you can't build a world model from a text, so he predicted that even "GPT 5000" can't predict that an object sitting on the table would move together with the table. But GPT-4 could solve this task and provided correct explanation. GPT-5 can solve much more complex tasks, code physics simulation, etc. Now people talk about invention of General Relativity as something LLMs can't do... something almost no human was able to do either.
- JacobAsmuth 2mo agoYou're hallucinating facts again.
- NewEntryHN 2mo agoYou don't need any reproduction. Assuming researchers are using LLMs, you should just see the number of jumps increase as models get better.
- aantix 2mo agoDon't these datasets already exist in terms of the real-world samplings we have when training robots to do every day tasks?
- yogthos 2mo agoThere's no reason why LLMs can't be hooked up to a physical world feedback loop though, in fact that's already being done with chemistry research https://www.youtube.com/watch?v=AYSR02tcwes https://www.youtube.com/watch?v=AYSR02tcwes
- gabbagool 2mo agoProbably too late for this, but I have argued before that language is a fundamentally lossy encoding of the human experience. We do our best to describe what we're seeing and experiencing using language, which is fantastically expressive, but it has its limits. I think we see glimpses of this when we find ourselves saying things such as, "it's impossible to put it into words" or we overload certain words when we mean very different things, such as, "I love my children" or "I love apple pie". Clearly the word "love" here has a certain magnitude that is not being expressed, yet it is understood by the listener somehow. So, I do sort of buy into this idea that Einstein was simulating the world and running experiments on those simulations in ways that were beyond what you could encode in natural language. Will AI be capable of doing this, if it is bounded by training data that is composed almost entirely on language? One might argue that if AI is training on a lossy encoding/representation of the human experience, how will it be able to simulate anything beyond that experience? Unless it does so in a way that we manage to do when we image objects beyond 3D. But now I'm just rambling.
- sandeepkd 2mo agoI think this is very critical concept and existing gap which does not makes into AI conversations. Interestingly enough a lot of Si Fi movies have captured this where the AI starts to feel and have "thoughts". Have to give kudos to the authors for being so creative and imaginative.
- gabbagool 2mo agoOr, ask yourself the following question: If you read every single book there is about The Grand Canyon, and watched every single video and/or documentary about The Grand Canyon, do you believe that you have fully experienced The Grand Canyon? Or do you just have to be there to fully experience it. I dunno. Substitute in whatever you want for "The Grand Canyon". Maybe climbing Mount Everest or walking on the Moon. The point is that maybe the human experience is more vast than what is written about it.
- Dylan16807 2mo agoI think I could get very close via video. But that depends on a lot of my non-canyon experience moving around the world, and LLMs are very flawed in their ability to input video, so I think they would not get nearly as close.
- peter_d_sherman 2mo ago>"While Generative AI has mastered Induction (statistical pattern matching) and is rapidly conquering Deduction (formal proof), we argue it lacks the mechanism for Abduction—the generation of novel explanatory hypotheses." Interesting! Induction Vs. Deduction Vs. Abduction! (You know, if you like Logic, Philosophy, Law, or... just plain different ways to think/reason about something! :-) )
- darkstarsys 2mo agoIf you think LLMs can't "jump," watch Terence Tao engage with ChatGPT to try to understand the intellectual leaps achieved by Claude Fable 5 in finding a counterexample to the Jacobian conjecture https://chatgpt.com/share/6a5fdc7a-d6f8-83e8-bbea-8deb42cfed56 https://chatgpt.com/share/6a5fdc7a-d6f8-83e8-bbea-8deb42cfed... . After that you may revise your opinion.
- JacobAsmuth 2mo agoI've said this for a long time. This inability of AIs to generate novel explanatory hypotheses is a big blocker for their ability to automate jobs like accountant, middle manager, and even the lowly cashier.
- brcmthrowaway 2mo agoCan talkie figure it out?
- Mikhail_Edoshin 2mo agoTrue understanding requires not knowing, and LLMs cannot "not know". LLMs have to come up with an answer, this is their nature. They are search engines. We do have a similar mechanism; one can notice it by reflecting. The mechanism is an opposite of true thinking, as it merely looks up what is already "known". We "jump" when we temporarily turn this mechanism off. That said, here's an experiment conducted by some Soviet psychologist, I forgot the name. The man wanted to study intuition. So he invented an experiment that was supposed to trigger it in laboratory conditions. (Take a moment to marvel at that; how would you approach such a task?) He gave people a few puzzles. One was to place some sticks according to some rules. Yet another was to find a path in a maze. The secret was that the path in the maze was the same figure as the solution to the stick puzzle. And he observed interesting results. People who solved the maze after the sticks found the path much faster than the control group. If a subject was asked to comment how he was solving the maze, at the start or halfway through, the speed dropped to typical. Subjects normally didn't notice the similarities. So there is something to study here, although it is obviously a case of pattern matching, only subconscious. This is a jump of sorts, but not the one I mean. What I mean is a Zen jump.
- shymaple 2mo ago[flagged]
- stardustss 2mo agoheavier-than-air flight is impossible