It's only easy to see precursors in hindsight. The Michelson-Morley tale is a great example of this. In hindsight, their experiment was screaming relativity, because it demonstrated that the speed of light was identical from two perspectives where it's very difficult to explain without relativity. Lorentz contraction was just a completely ad-hoc proposal to maintain the assumptions of the time (luminiferous aether in particular) while also explaining the result. But in general it was not seen as that big of a deal.

There's a very similar parallel with dark matter in modern times. We certainly have endless hints to the truth that will be evident in hindsight, but for now? We are mostly convinced that we know the truth, perform experiments to prove that, find nothing, shrug, adjust the model to be even more esoteric, and repeat onto the next one. And maybe one will eventually show something, or maybe we're on the wrong path altogether. This quote, from Michelson in 1894 (more than a decade before Einstein would come along), is extremely telling of the opinion at the time:

"While it is never safe to affirm that the future of Physical Science has no marvels in store even more astonishing than those of the past, it seems probable that most of the grand underlying principles have been firmly established and that further advances are to be sought chiefly in the rigorous application of these principles to all the phenomena which come under our notice. It is here that the science of measurement shows its importance — where quantitative work is more to be desired than qualitative work. An eminent physicist remarked that the future truths of physical science are to be looked for in the sixth place of decimals." - Michelson 1894

I presume that's what the parent post is trying to get at? Seeing if, given the cutting edge scientific knowledge of the day, the LLM is able to synthesis all it into a workable theory of QM by making the necessary connections and (quantum...) leaps

Standing on the shoulders of giants, as it were

If (as you seem to be suggesting) relativity was effectively lying there on the table waiting for Einstein to just pick it up, how come it blindsided most, if not quite all, of the greatest minds of his generation?

This would still be valuable even if the LLM only finds out about things that are already in the air.

It’s probably even more of a problem that different areas of scientific development don’t know about each other. LLMs combining results would still not be like they invented something new.

But if they could give us a head start of 20 years on certain developments this would be an awesome result.

With LLMs the synthesis cycles could happen at a much higher frequency. Decades condensed to weeks or days?

I imagine possible buffers on that conjecture synthesis being epxerimentation and acceptance by the scientific community. AIs can come up with new ideas every day but Nature won't publish those ideas for years.

Then that experiment is even more interesting, and should be done.

My own prediction is that the LLMs would totally fail at connecting the dots, but a small group of very smart humans can.

Things don't happen all of a sudden, but they also don't happen everywhere. Most people in most parts of the world would never connect the dots. Scientific curiosity is something valuable and fragile, that we just take for granted.

I agree, but it's important to note that QM has no clear formulation until 2025/6, it's like 20 years more of work than SR.

I am a historian and am putting together a grant application for a somewhat similar project (different era and language though). Would you be open to discussing a collaboration? My email is bebreen [at] ucsc [dot] edu.

Can we follow along with your work / results somewhere?

Look! It made another TODO-list app on the first try!

And unfortunately I don't think they plan on making those models public.

It doesn’t need to know about QM or reactivity just about the building blocks that led to them. Which were more than around in the year 1900.

In fact you don’t want it to know about them explicitly just have enough background knowledge that you can manage the rest via context.

I'm not sure what you'd call a "pioneering scientific advancement", but there is an increasing amount of examples showing that LLMs can be used for research (with agents, particularly). A survey about this was published a few months ago: https://aclanthology.org/2025.emnlp-main.895.pdf

Ofc they are because their primary goal is to be useful and to be useful they need to always be relevant.

But considering that Special Relativity was published in 1905 which means all its building blocks were already floating in the ether by 1900 it would be a very interesting experiment to train something on Claude/Gemini scale and then say give in the field equations and ask it to build a theory around them.

I don't know if this is related to the topic, but GPT5 can convert an 1880 Ottoman archival photograph to English, and without any loss of quality.

There is some ability for it to make novel connections but it's pretty small. You can see this yourself having it build novel systems.

It largely cannot imaginr anything beyond the usual but there is a small part that it can. This is similar to in context learning, it's weak but it is there.

It would be incredible if meta learning/continual learning found a way to train exactly for novel learning path. But that's literally AGI so maybe 20yrs from now? Or never..

You can see this on CL benchmarks. There is SOME signal but it's crazy low. When I was traing CL models i found that signal was in the single % points. Some could easily argue it was zero but I really do believe there is a very small amount in there.

This is also why any novel work or findings is done via MASSIVE compute budgets. They find RL enviroments that can extract that small amount out. Is it random chance? Maybe, hard to say.

How about this for an evaluation: Have this (trained-on-older-corpus) LLM propose experiments. We "play the role of nature" and inform it of the results of the experiments. It can then try to deduce the natural laws.

If we did this (to a good enough level of detail), would it be able to derive relativity? How large of an AI model would it have to be to successfully derive relativity (if it only had access to everything published up to 1904)?

Preface: Most of my understand of how LLMs actually work comes from 3blue1brown's videos, so I could easily be wrong here.

I mostly agree with you, especially about distrusting the self-interested hype beasts.

While I don't think the models are actually "intelligent", I also wonder if there are insights to be gained by looking at how concepts get encoded by the models. It's not really that the models will add something "new", but more that there might be connections between things that we haven't noticed, especially because academic disciplines are so insular these days.

What do they (or you) have to say about the Lee Sedol AlphaGo move 78. It seems like that was "new knowledge." Are games just iterable and the real world idea space not? I am playing with these ideas a little.

Do you have a pointer to where LeCun spoke about it? I noticed last October that Dwarkesh mentioned the idea off handedly on his podcast (prompting me to write up https://manifold.markets/MikeLinksvayer/llm-trained-on-data-...) but I wonder if this idea has been around for much longer, or is just so obvious that lots of people are independently coming up with it (parent to this comment being yet another)?

This is definitely wrong, most AI researchers DO NOT agree with LeCun.

Most ML researchers think AGI is imminent.

i will go for ‘aint gonna happen for a 1000 dollars alex’