I 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.
Position: LLMs Can't Jump
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Re: Position: LLMs Can't Jump
#32I 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.
Re: Position: LLMs Can't Jump
#33It'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.
Re: Position: LLMs Can't Jump
#34The 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.
Re: Position: LLMs Can't Jump
#35I 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.
Technically 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.
Re: Position: LLMs Can't Jump
#36Came 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.
Is that how chessboxing was invented? Genuinely asking.
Re: Position: LLMs Can't Jump
#37Came 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.
I 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
Maybe we can synthesize large amounts of limited information. I thought that new training data is mostly synthetic anyway.
Re: Position: LLMs Can't Jump
#38Earlier quoted context omitted.
I 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
Even simpler: Can GPT-2 anticipate and build Gwen/Deepseek? I think the answer is almost trivially "no", so I wonder what changed?
Lots 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.
Re: Position: LLMs Can't Jump
#39LLMs can’t but humans supplied with data and reasoning from an LLM can make novel jumps without absolute prior knowledge, or at least with reduced need for years of knowledge.
And then such jump can be verified by a machine so human kind of plugs the intelligence gap.
That’s pretty exciting.
I always liked to provocatively call LLM „the new calculator”. Calculator for language.
We are so focused on creating a standalone intelligence that we didn’t notice how we massively augmented our own. That could be considered transhumanism holy grail if only interface brain-LLM was faster.
People need to understand that these things are tools. And every tool needs an operator to function. Tool doesn’t have its own goals, needs, wants or motives. It won’t do anything out of its own, it always exists in context of someone telling it what to do.
In light of that most of the panic and fear mongering is rather ridiculous. Calculator won’t replace you. It wont take over the world. It is just a tool.
You write a book with book generator? Cool, it can be used for this. We will judge output, not the methods. Sometimes we will judge people who have no taste in literature.
Re: Position: LLMs Can't Jump
#40Interestingly, halucinations might be the way to achieve that.