alt Hacker NewsThis is a way too simplistic model of the things humans provide to the process. Imagination, Hypothesis, Testing, Intuition, and Proofing.
An AI can probably do an 'okay' job at summarizing information for meta studies. But what it can't do is go "Hey that's a weird thing in the result that hints at some other vector for this thing we should look at." Especially if that "thing" has never been analyzed before and there's no LLM-trained data on it.
LLMs will NEVER be able to do that, because it doesn't exist. They're not going to discover and define a new chemical, or a new species of animal. They're not going to be able to describe and analyze a new way of folding proteins and what implication that has UNLESS you basically are constantly training the AI on random protein folds constantly.
Replies
>Hey that's a weird thing in the result that hints at some other vector for this thing we should look at
Kinda funny because that looked _very_ close to what my Opus 4.6 said yesterday when it was debugging compile errors for me. It did proceed to explore the other vector.
""Hey that's a weird thing in the result that hints at some other vector for this thing we should look at." "
This is very common already in AI.
Just look at the internal reasoning of any high thinking model, the trace is full of those chains of thought.
But just like how there were never any clips of Will Smith eating spaghetti before AI, AI is able to synthesize different existing data into something in between. It might not be able to expand the circle of knowledge but it definitely can fill in the gaps within the circle itself
> LLMs will NEVER be able to do that, because it doesn't exist.
I mean, TFA literally claims that an AI has solved an open Frontier Math problem, descibed as "A collection of unsolved mathematics problems that have resisted serious attempts by professional mathematicians. AI solutions would meaningfully advance the state of human mathematical knowledge."
That is, if true, it reasoned out a proof that does not exist in its training data.
[dead]
I think you are vastly underestimating the emergent behaviours in frontier foundational models and should never say never.
Remember, the basis of these models is unsupervised training, which, at sufficient scale, gives it the ability to to detect pattern anomalies out of context.
For example, LLMs have struggled with generalized abstract problem solving, such as "mystery blocks world" that classical AI planners dating back 20+ years or more are better at solving. Well, that's rapidly changing: https://arxiv.org/html/2511.09378v1