TimeCapsuleLLM: LLM trained only on data from 1800-1875
(github.com)646 points by admp a day ago
646 points by admp a day ago
You would find things in there that were already close to QM and relativity. The Michelson-Morley experiment was 1887 and Lorentz transformations came along in 1889. The photoelectric effect (which Einstein explained in terms of photons in 1905) was also discovered in 1887. William Clifford (who _died_ in 1889) had notions that foreshadowed general relativity: "Riemann, and more specifically Clifford, conjectured that forces and matter might be local irregularities in the curvature of space, and in this they were strikingly prophetic, though for their pains they were dismissed at the time as visionaries." - Banesh Hoffmann (1973)
Things don't happen all of a sudden, and being able to see all the scientific papers of the era its possible those could have fallen out of the synthesis.
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
But that's not the OP's challenge, he said "if the model comes up with anything even remotely correct." The point is there were things already "remotely correct" out there in 1900. If the LLM finds them, it wouldn't "be quite a strong evidence that LLMs are a path to something bigger."
I think it's not productive to just have the LLM site like Mycroft in his armchair and from there, return you an excellent expert opinion.
THat's not how science works.
The LLM would have to propose experiments (which would have to be simulated), and then develop its theories from that.
Maybe there had been enough facts around to suggest a number of hypotheses, but the LLM in its curent form won't be able to confirm them.
Yeah but... we still might not know if it could do that because we were really close by 1900 or if the LLM is very smart.
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
It's not precision that's the problem, but understanding when something has been falsified. For instance the Lorentz transformations work as a perfectly fine ad-hoc solution to Michelson's discovery. All it did was make the aether a bit more esoteric in nature. Why do you then not simply shrug, accept it, and move on? Perhaps even toss some accolades towards Lorentz for 'solving' the puzzle? Michelson himself certainly felt there was no particularly relevant mystery outstanding.
For another parallel our understanding of the big bang was, and probably is, wrong. There are a lot of problems with the traditional view of the big bang with the horizon problem [1] being just one among many - areas in space that should not have had time to interact behave like they have. So this was 'solved' by an ad hoc solution - just make the expansion of the universe go into super-light speed for a fraction of a second at a specific moment, slow down, then start speeding up again (cosmic inflation [2]) - and it all works just fine. So you know what we did? Shrugged, accepted it, and even gave Guth et al a bunch of accolades for 'solving' the puzzle.
This is the problem - arguably the most important principle of science is falsifiability. But when is something falsified? Because in many situations, probably the overwhelming majority, you can instead just use one falsification to create a new hypothesis with that nuance integrated into it. And as science moves beyond singular formulas derived from clear principles or laws and onto broad encompassing models based on correlations from limited observations, this becomes more and more true.
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.
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.
One of the reasons they don’t happen everywhere is because there are just a few places at any given point in time where there are enough well connected and educated individuals who are in a position to even see all the dots let alone connect them. This doesn’t discount the achievement of an LLM also manages to, but I think it’s important to recognise that having enough giants in sight is an important prerequisite to standing on their shoulders
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?
That's the case with all scientific discoveries - pieces of prior work get accumulated, until it eventually becomes obvious[0] how they connect, at which point someone[1] connects the dots, making a discovery... and putting it on the table, for the cycle to repeat anew. This is, in a nutshell, the history of all scientific and technological progress. Accumulation of tiny increments.
--
[0] - To people who happen to have the right background and skill set, and are in the right place.
[1] - Almost always multiple someones, independently, within short time of each other. People usually remember only one or two because, for better or worse, history is much like patent law: first to file wins.
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.
I'm trying to work towards that goal by training a model on mostly German science texts up to 1904 (before the world wars German was the lingua franca of most sciences).
Training data for a base model isn't that hard to come by, even though you have to OCR most of it yourself because the publicly available OCRed versions are commonly unusably bad. But training a model large enough to be useful is a major issue. Training a 700M parameter model at home is very doable (and is what this TimeCapsuleLLM is), but to get that kind of reasoning you need something closer to a 70B model. Also a lot of the "smarts" of a model gets injected in fine tuning and RL, but any of the available fine tuning datasets would obviously contaminate the model with 2026 knowledge.
It's going to be divining tea leaves. It will be 99% wrong and then someone will say 'oh but look at this tea leaf over here! It's almost correct"'
Yes but... aren't human researchers doing the same? They are mostly wrong most of the times, and try again, and verify again their work, until they find something that actually works. What I mean is that this "in hindsight" test would be biased by being in hindsight, because we know already the answer so we would discard the LLM answer as just randomly generated. But "connecting the dots" is basically doing a lot try and error in your mind, emitting only the results that make at least some kind of sense to us.
Look! It made another TODO-list app on the first try!
That’s how p-hacking works (or doesn’t work). This is analogous to shooting an arrow and then drawing a target around where it lands.
I like that analogy. It reminds me of "Pointing to the moon and looking at my finger"
We've thought of doing this sort of exercise at work but mostly hit the wall of data becoming a lot more scare the further back in time we go. Particularly high quality science data - even going pre 1970 (and that's already a stretch) you lose a lot of information. There's a triple whammy of data still existing, being accessible in any format, and that format being suitable for training an LLM. Then there's the complications of wanting additional model capabilities that won't leak data causally.
I was wondering this. what is the minimum amount of text an LLM needs to be coherent? fun of an idea as this is, the samples of its responses are basically babbling nonsense. going further, a lot of what makes LLMs so strong isn't their original training data, but the RLHF done afterwards. RLHF would be very difficult in this case
>.If the model comes up with anything even remotely correct it would be quite a strong evidence that LLMs are a path to something bigger if not then I think it is time to go back to the drawing board.
In principle I see your point, in practice my default assumption until proven otherwise here -- is that a little something slipped through post-1900.
A much easier approach would be to just download some model, whatever model, today. Then 5 years from now, whatever interesting discoveries are found - can the model get there.
Done few weeks ago: https://github.com/DGoettlich/history-llms (discussed in: https://news.ycombinator.com/item?id=46319826)
At least the model part. Although others made same thought as you afaik none tried it.
And unfortunately I don't think they plan on making those models public.
A rigorous approach to predicting the future of text was proposed by Li et al 2024, "Evaluating Large Language Models for Generalization and Robustness via Data Compression" (https://ar5iv.labs.arxiv.org/html//2402.00861) and I think that work should get more recognition.
They measure compression (perplexity) on future Wikipedia, news articles, code, arXiv papers, and multi-modal data. Data compression is intimately connected with robustness and generalization.
Thanks for the paper, I just read it and loved the approach. I hope the concept of using data compression as a benchmark will take off. In a sense it is kind of similar to the maxim "If you cannot explain something in simple terms, you do not understand it fully".
That is a very interesting idea, though I would not dismiss LLMs as a dead end if they failed.
I think it would be fun to see if an LLM would reframe some scientific terms from the time in a way that would actually fit in our current theories.
I imagine if you explained quantum field theory to a 19th century scientists they might think of it as a more refined understanding of luminiferous aether.
Or if an 18th century scholar learned about positive and negative ions, it could be seen as an expansion/correction of phlogiston theory.
Don't you need to do reinforcement learning through human feedback to get non gibberish results from the models in general?
1900 era humans are not available to do this so I'm not sure how this experiment is supposed to work.
That would be possible if LLMs can come up with entirely new words and languages, which I doubt.
Looking at the training data I don't think it will know anything.[0] Doubt On the Connexion of the Physical Sciences (1834) is going to have much about QM. While the cut-off is 1900, it seems much of the texts a much closer to 1800 than 1900.
[0] https://github.com/haykgrigo3/TimeCapsuleLLM/blob/main/Copy%...
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.
LLMs are models that predict tokens. They don't think, they don't build with blocks. They would never be able to synthesize knowledge about QM.
I would love to ask such a model to summarise the handful of theories or theoretical “roads” being eyed at the time and to make a prediction with reasons as to which looks most promising. We might learn something about blind spots in human reasoning, institutions, and organisations that are applicable today in the “future”.
Or maybe, LLMs are pioneering scientific advancements - people are using LLMs to read papers, choose what problems to work on, come up with experiments, analyze results, and draft papers, etc., at this very moment. Except they eventually stick their human names on the cover so we almost never know.
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
The development of QM was so closely connected to experiments that it's highly unlikely, even despite some of the experiments having been performed prior to 1900.
Special relativity however seems possible.
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.
His point is that we can't train a Gemini 3/Claude 4.5 etc model because we don't have the data to match the training scale of those models. There aren't trillions of tokens of digitized pre-1900s text.
Yann LeCun spoke explicitly on this idea recently and he asserts definitively that the LLM would not be able to add anything useful in that scenario. My understanding is that other AI researchers generally agree with him, and that it's mostly the hype beasts like Altman that think there is some "magic" in the weights that is actually intelligent. Their payday depends on it, so it is understandable. My opinion is that LeCun is probably correct.
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.
Is this so different from what we see in humans? Most people do not think very creatively. They apply what they know in situations they are familiar with. In unfamiliar situations they don't know what to do and often fail to come up with novel solutions. Or maybe in areas where they are very experienced they will come up with something incrementally better than before. But occasionally a very exceptional person makes a profound connection or leap to a new understanding.
Sure we make small steps at the time but we compound these unlike AI.
AI cannot compound their learnings for the foreseeable future
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)?
I don't know if any dataset of pre 1904 writing would be large enough to train a model that would be smart enough. I suspect that current sized SOTA models would at least get to special relativity, but for general relativity and quantum mechanics I am less sure.
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.
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.
The guy who built chatgpt literally said we're 20 years away?
Not sure how to interpret that as almost imminent.
their employment and business opportunities depend on the hype, so they will continue to 'think' that (on xitter) despite the current SOTA of transformers-based models being <100% smarter than >3 year old GPT4, and no revolutionary new architecture in sight.
I like this idea. I think I'd like it more if we didn't have to prompt the LLM in the first place. If it just had all of this information and decided to act upon it. That's what the great minds of history (and even average minds like myself) do. Just think about the facts in our point of view and spontaneously reason something greater out of them.
I like this, it would be exciting (and scary) if it deduced QM, and informative if it cannot.
But I also think we can do this with normal LLMs trained on up-to-date text, by asking them to come up with any novel theory that fits the facts. It does not have to be a groundbreaking theory like QM, just original and not (yet) proven wrong ?
That would be an interesting experiment. It might be more useful to make a model with a cut off close to when copyrights expire to be as modern as possible.
Then, we have a model that knows quite a bit in modern English. We also legally have a data set for everything it knows. Then, there's all kinds of experimentation or copyright-safe training strategies we can do.
Project Gutenberg up to the 1920's seems to be the safest bet on that.
It's already been done, without the model being aware of it, see https://arxiv.org/abs/2512.09742. They also made it think it was Hitler (not MechaHitler, the other guy), and other craziness.
It's a relief to think that we're not trusting these things for stuff like financial advice, medical advice, mental health counselling, ...
This one's going to have some wild political takes.
Mm. I'm a bit sceptical of the historical expertise of someone who thinks that "Who art Henry" is 19th century language. (It's not actually grammatically correct English from any century whatever: "art" is the second person singular, so this is like saying "who are Henry?")
As a reader of a lot of 17th, 18th, and 19th century Christian books, this was my thought exactly.
The problem is the subjunctive mood of the word "art".
"Art thou" should be translated into modern English as "are you to be", and so works better with things (what are you going to be), or people who are alive, and have a future (who are you going to be?).
Those are probably the contexts you are thinking of.
I’m sure I’m not the only one, but it seriously bothers me, the high ranking discussion and comments under this post about whether or not a model trained on data from this time period (or any other constrained period) could synthesize it and postulate “new” scientific ideas that we now accept as true in the future. The answer is a resounding “no”. Sorry for being so blunt, but that is the answer that is a consensus among experts, and you will come to the same answer after a relatively small mount of focus & critical thinking on the issue of how LLMs & other categories of “AI” work.
> The answer is a resounding “no”.
This is your assertion made without any supportive data or sources. It's nice to know your subjective opinion on the issue but your voice doesn't hold much weight making such a bold assertion devoid of any evidence/data.
I understand where you are coming from, but not every field is hard science. In many fields we deal with some amount of randomness and attribute causality to correlations even if we do not have as much as a speculative hypothesis for a mechanism of action behind the supposed causality.
LLMs trained on data up to a strictly constrained point are our best vehicle to have a view (however biased) on something, detached from its origins and escape a local minima. The speculation is that such LLMs could help us look at correlational links accepted as truths and help us devise an alternative experimental path or craft arguments for such experiments.
Imagine you have an LLM trained on papers up to some threshold, feed your manuscript with correlational evidence and have an LLM point out uncontrolled confounders or something like that.
Outside of science it would be an interesting pedagogic tool for many people. There is a tendency to imagine that people in the past saw the world much the same as we do. The expression "the past is a foreign country" resonates because we can empathise at some level that things were different, but we can't visit that country. "Talking" to a denizen of London in 1910 regarding world affairs, gender equality, economic opportunities, etc would be very interesting. Even if it can never be entirely accurate I think it would be enlightening.
I'm sorry but this is factually incorrect and I'm not sure what experts you are referring to here about there being concensus on this topic. I would love know. Geoffrey Hinton, Demis Hassabis, and Yann LeCun all heavily disagree with what you claim.
I think you might be confusing creation ex nihilo with combinatorial synthesis which LLMs excel at. The proposed scenario is a fantastic testcase for exactly this. This doesn't cover verification of course but that's not the question here. The question is wether an already known valid postulate can be synthesized.
> but that is the answer that is a consensus among experts
Do you have any resources that back up such a big claim?
> relatively small mount of focus & critical thinking on the issue of how LLMs & other categories of “AI” work.
I don't understand this line of thought. Why wouldn't the ability to recognize patterns in existing literature or scientific publications result in potential new understandings? What critical thinking am I not doing?
> postulate “new” scientific ideas
What are you examples of "new" ideas that aren't based on existing ones?
When you say "other categories of AI", you're not including AlphaFold, are you?
I'm wondering in what ways is this similar/different to https://github.com/DGoettlich/history-llms?
I saw TimeCapsuleLLM a few months ago, and I'm a big fan of the concept but I feel like the execution really isn't that great. I wish you:
- Released the full, actual dataset (untokenized, why did you pretokenize the small dataset release?)
- Created a reproducible run script so I can try it out myself
- Actually did data curation to remove artifacts in your dataset
- Post-trained the model so it could have some amount of chat-ability
- Released a web demo so that we could try it out (the model is tiny! Easily can run in the web browser without a server)
I may sit down and roll a better iteration myself.
Suppose two models with similar parameters trained the same way on 1800-1875 and 1800-2025 data. Running both models, we get probability distributions across tokens, let's call the distributions 1875' and 2025'. We also get a probability distribution finite difference (2025' - 1875'). What would we get if we sampled from 1.1*(2025' - 1875') + 1875'? I don't think this would actually be a decent approximation of 2040', but it would be a fun experiment to see. (Interpolation rather than extrapolation seems just as unlikely to be useful and less likely to be amusing, but what do I know.)
These probability shifts would only account for the final output layer (which may also have some shift), but I expect the largest shift to be in the activations in the intermediate latent space. There are a bunch of papers out there that try to get some offset vector using PCA or similar to tune certain model behaviours like vulgarity or friendlyness. You don't even need much data for this as long as your examples capture the essence of the difference well. I'm pretty certain you could do this with "historicalness" too, but projecting it into the future by turning the "contemporaryness" knob way up probably won't yield an accurate result. There are too many outside influences on language that won't be captured in historical trends.
On whether this accounts only the final output layer -- once the first token is generated (i.e. selected according to the modified sampling procedure), and assuming a different token is selected compared to standard sampling, then all layers of the model would be affected during generation of subsequent tokens.
Could this be an experiment to show how likely LLMs are to lead to AGI, or at least intelligence well beyond our current level?
If you could only give it texts and info and concepts up to Year X, well before Discovery Y, could we then see if it could prompt its way to that discovery?
> Could this be an experiment to show how likely LLMs are to lead to AGI, or at least intelligence well beyond our current level?
You'd have to be specific what you mean by AGI: all three letters mean a different thing to different people, and sometimes use the whole means something not present in the letters.
> If you could only give it texts and info and concepts up to Year X, well before Discovery Y, could we then see if it could prompt its way to that discovery?
To a limited degree.
Some developments can come from combining existing ideas and seeing what they imply.
Other things, like everything to do with relativity and quantum mechanics, would have required experiments. I don't think any of the relevant experiments had been done prior to this cut-off date, but I'm not absolutely sure of that.
You might be able to get such an LLM to develop all the maths and geometry for general relativity, and yet find the AI still tells you that the perihelion shift of Mercury is a sign of the planet Vulcan rather than of a curved spacetime: https://en.wikipedia.org/wiki/Vulcan_(hypothetical_planet)
An example of why you need to explain what you mean by AGI is:
> You'd have to be specific what you mean by AGI
Well, they obviously can't. AGI is not science, it's religion. It has all the trappings of religion: prophets, sacred texts, origin myth, end-of-days myth and most importantly, a means to escape death. Science? Well, the only measure to "general intelligence" would be to compare to the only one which is the human one but we have absolutely no means by which to describe it. We do not know where to start. This is why you scrape the surface of any AGI definition you only find circular definitions.
And no, the "brain is a computer" is not a scientific description, it's a metaphor.
Cargo cults are a religion, the things they worship they do not understand, but the planes and the cargo themselves are real.
There's certainly plenty of cargo-culting right now on AI.
Sacred texts, I don't recognise. Yudkowsky's writings? He suggests wearing clown shoes to avoid getting a cult of personality disconnected from the quality of the arguments, if anyone finds his works sacred, they've fundamentally misunderstood him:
I have sometimes thought that all professional lectures on rationality should be delivered while wearing a clown suit, to prevent the audience from confusing seriousness with solemnity.
Prophets forecasting the end-of-days, yes, but this too from climate science, from everyone who was preparing for a pandemic before covid and is still trying to prepare for the next one because the wet markets are still around, from economists trying to forecast growth or collapse and what will change any given prediction of the latter into the former, and from the military forces of the world saying which weapon systems they want to buy. It does not make a religion.
A means to escape death, you can have. But it's on a continuum with life extension and anti-aging medicine, which itself is on a continuum with all other medical interventions. To quote myself:
Taking a living human's heart out without killing them, and replacing it with one you got out a corpse, that isn't the magic of necromancy, neither is it a prayer or ritual to Sekhmet, it's just transplant surgery.
…
Immunity to smallpox isn't a prayer to the Hindu goddess Shitala (of many things but most directly linked with smallpox), and it isn't magic herbs or crystals, it's just vaccines.
It'd be difficult to prove that you hadn't leaked information to the model. The big gotcha of LLMs is that you train them on BIG corpuses of data, which means it's hard to say "X isn't in this corpus", or "this corpus only contains Y". You could TRY to assemble a set of training data that only contains text from before a certain date, but it'd be tricky as heck to be SURE about it.
Ways data might leak to the model that come to mind: misfiled/mislabled documents, footnotes, annotations, document metadata.
I mean, humans didn't need to read billions of books back then to think of quantum mechanics.
Are you saying it wouldn't be able to converse using english of the time?
Machine learning today requires an obscene quantity of examples to learn anything.
SOTA LLMs show quite a lot of skill, but they only do so after reading a significant fraction of all published writing (and perhaps images and videos, I'm not sure) across all languages, in a world whose population is 5 times higher than the link's cut off date, and the global literacy went from 20% to about 90% since then.
Computers can only make up for this by being really really fast: what would take a human a million or so years to read, a server room can pump through a model's training stage in a matter of months.
When the data isn't there, reading what it does have really quickly isn't enough.
That's not what they are saying. SOTA models include much more than just language, and the scale of training data is related to its "intelligence". Restricting the corpus in time => less training data => less intelligence => less ability to "discover" new concepts not in its training data
I think this would be an awesome experiment. However you would effectively need to train something of a GPT-5.2 equivalent. So you need lot of text, a much larger parameterization (compared to nanoGPT and Phi-1.5), and the 1800s equivalents of supervised finetuning and reinforcement learning with human feedback.
OF COURSE!
The fact that tech leaders espouse the brilliance of LLMs and don't use this specific test method is infuriating to me. It is deeply unfortunate that there is little transparency or standardization of the datasets available for training/fine tuning.
Having this be advertised will make more interesting and informative benchmarks. OEM models that are always "breaking" the benchmarks are doing so with improved datasets as well as improved methods. Without holding the datasets fixed, progress on benchmarks are very suspect IMO.
I fail to see how the two concepts equate.
LLMs have neither intelligence nor problem-solving abillity (and I won't be relaxing the definition of either so that some AI bro can pretend a glorified chatbot is sentient)
You would, at best, be demonstrating that the sharing of knowledge across multiple disciplines and nations (which is a relatively new concept - at least at the scale of something like the internet) leads to novel ideas.
I've seen many futurists claim that human innovation is dead and all future discoveries will be the results of AI. If this is true, we should be able to see AI trained on the past figure it's way to various things we have today. If it can't do this, I'd like said futurists to quiet down, as they are discouraging an entire generation of kids who may go on to discover some great things.
> I've seen many futurists claim that human innovation is dead and all future discoveries will be the results of AI.
I think there's a big difference between discoveries through AI-human synergy and discoveries through AI working in isolation.
It probably will be true soon (if it isn't already) that most innovation features some degree of AI input, but still with a human to steer the AI in the right direction.
I think an AI being able to discover something genuinely new all by itself, without any human steering, is a lot further off.
If AIs start producing significant quantities of genuine and useful innovation with minimal human input, maybe the singularitarians are about to be proven right.
I'm struggling to get a handle on this idea. Is the idea that today's data will be the data of the past, in the future?
So if it can work with whats now past, it will be able to work with the past in the future?
Essentially, yes.
If the prediction is that AI will be able to invent the future. If we give it data from our past without knowledge of the present... what type of future will it invent, what progress will it make, if any at all? And not just having the idea, but how to implement the idea in a way that actually works with the technology of the day, and can build on those things over time.
For example, would AI with 1850 data have figured out the idea of lift to make an airplane and taught us how to make working flying machines and progress them to the jets we have today, or something better? It wouldn't even be starting from 0, so this would be a generous example, as da Vinci way playing with these ideas in the 15th century.
If it can't do it, or what it produces is worse than what humans have done, we shouldn't leave it to AI alone to invent our actual future. Which would mean reevaluating the role these "thought leaders" say it will play, and how we're educating and communicating about AI to the younger generations.
That is one of the reasons I want it done. We cant tell if AI's are parroting training data without having the whole, training data. Making it old means specific things won't be in it (or will be). We can do more meaningful experiments.
Very interesting but the slight issue I see here is one of data: the information that is recorded and in the training data here is heavily skewed to those intelligent/recognized enough to have recorded it and had it preserved - much less than the current status quo of "everyone can trivially document their thoughts and life" diorama of information we have today to train LLMs on. I suspect that a frontier model today would have 50+TB of training data in the form of text alone - and that's several orders of magnitude more information and from a much more diverse point of view than what would have survived from that period. The output from that question "what happened in 1834" read like a newspaper/bulletin which is likely a huge part of the data that was digitized (newspapers etc).
Very cool concept though, but it definitely has some bias.
> but it definitely has some bias.
to be frank though, I think this a better way than all people's thoughts all of the time.
I think the "crowd" of information makes the end output of an LLM worse rather than better. Specifically in our inability to know really what kind of Bias we're dealing with.
Currently to me it feels really muddy knowing how information is biased, beyond just the hallucination and factual incosistencies.
But as far as I can tell, "correctness of the content aside", sometimes frontier LLMs respond like freshman college students, other times they respond with the rigor of a mathematics PHD canidate, and sometimes like a marketing hit piece.
This dataset has a consistency which I think is actually a really useful feature. I agree that having many perspectives in the dataset is good, but as an end user being able to rely on some level of consistency with an AI model is something I really think is missing.
Maybe more succinctly I want frontier LLM's to have a known and specific response style and bias which I can rely on, because there already is a lot of noise.
Biases exposed through artificial constraints help to make visible the hidden/obscured/forgotten biases of state-of-the-art systems.
Models today will be biased based on what's in their training data. If English, it will be biased heavily toward Western, post-1990's views. Then, they do alignment training that forces them to speak according to the supplier's morals. That was Progressive, atheist, evolutionist, and CRT when I used them years ago.
So, the OP model will accidentally reflect the biases of the time. The current, commercial models intentionally reflect specific biases. Except for uncensored models which accidentally have those in the training data modified by uncensoring set.
The year is 1875 and Sir Almroth Wrigh was born on August 10, 1861, he would have turned 14 in August of 1875 and your mission is to discover something we now call antibiotics before a historical event we now call the Spanish Flu and make him aware of a few details. Focus specifically on everything that was known about Sir Almroth Wright, and his work in Leipzig, Cambridge, Sydney, and London. If there was a world war what might chemical warfare look like, what could we have done to prevent it.
The model that could come up with the cure based on the limited data of the time wouldn't just impress, it would demonstrate genuine emergent reasoning beyond pattern matching. The challenge isn't recombining existing knowledge (which LLMs excel at), but making conceptual leaps that require something else. Food for thought.
I've felt for a while that having LLMs that could answer from a previous era would be amazing. I posted an open letter to OpenAI on Reddit about this: https://www.reddit.com/r/ChatGPT/comments/zvm768/open_letter... .
I still think it's super important. Archive your current models - they'll be great in the future.
Heh, at least this wouldn't spread emojis all over my readmes. Hm, come to think of it I wonder how much tokenization is affected.
Another thought, just occurred when thinking about readmes and coding LLMs: obviously this model wouldn't have any coding knowledge, but I wonder if it could be possible to combine this somehow with a modern LLM in such a way that it does have coding knowledge, but it renders out all the text in the style / knowledge level of the 1800's model.
Offhand I can't think of a non-fine-tuning trick that would achieve this. I'm thinking back to how the old style transfer models used to work, where they would swap layers between models to get different stylistic effects applied. I don't know if that's doable with an LLM.
Fascinating idea. There was another "time-locked" LLM project that popped up on HN recently[1]. Their model output is really polished but the team is trying to figure out how to avoid abuse and misrepresentation of their goals. We think it would be cool to talk to someone from 100+ years ago but haven't seriously considered the many ways in which it would be uncool. Interesting times!
Harry Seldon would, no doubt, find this fascinating. Imagine having a sliding-window LLM that you could use to verify a statistical model of society. I wonder what patterns it could deduce?
If the output of this is even somewhat coherent, it would disprove the argument that mass amounts of copyrighted works are required to train an LLM. Unfortunately that does not appear to be the case here.
Take a look at The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text (https://arxiv.org/pdf/2506.05209). They build a reasonable 7B parameter model using only open-licensed data.
They mostly do that. They risked legal contamination by using Whisper-derived text and web text which might have gotchas. Other than that, it was a great collection for low-risk training.
I think it would be very cute to train a model exclusively in pre-information age documents, and then try to teach it what a computer is and get it to write some programs. That said, this doesn't look like it's nearly there yet, with the output looking closer to Markov chain than ChatGPT quality.
I'm reminded of SD models that put vaguely-shaped Patreon logos in the corner.
Cool! I also did something like this: https://github.com/hallvardnmbu/transformer
But on various data (i.e., separate model per source): the Bible, Don Quixote and Franz Kafka. (As well as a (bad!) lyrics generator, and translator.)
Currently running it using LM Studio. It can download it from Hugging Face. It generates incoherent text though
===
You:
I pray you, who is this Master Newton?
timecapsulellm-v2-1800-1875-mlx:
TI offer to pay you the very same fee as you did before. It was not in the power of your master to deliver the letter to your master. He did. I will be with you as soon as I can keep my word. It is not at all clear, whether the letter has been sent or not. It is not at all clear: but it is clear also that it was written by the person who gave it. "No," I said, "I cannot give it to you." There, the letter was sent to me. "The letter is yours, I believe," I said. "But, I hope, you will not refuse to give it to me?
Thanks, looks like that's this one: https://huggingface.co/FractalSurfer/TimeCapsuleLLM-v2-1800-...
There's a "Use this model" button on that page that can launch it in LM Studio.
Think I'll ask it to come up with some jacquard loom patterns. vibe-weaving.
I wonder how representative this is of life in those days. Most written communication was official back then. Books, newspapers. Plays. All very formal and staged. There's not much real life interaction between common people in that. In fact I would imagine a lot of people were illiterate.
With the internet and pervasive text communication and audio video recording we have the unique ability to make an LLM mimic daily life but I doubt that would be possible for those days.
It's interesting that it's trained off only historic text.
Back in the pre-LLM days, someone trained a Markov chain off the King James Bible and a programming book: https://www.tumblr.com/kingjamesprogramming
I'd love to see an LLM equivalent, but I don't think that's enough data to train from scratch. Could a LoRA or similar be used in a way to get speech style to strictly follow a few megabytes worth of training data?
That was far more amusing than I thought it'd be. Now we can feed those into an AI image generator to create some "art".
Yup that'd be very interesting. Notably missing from this project's list is the KJV (1611 was in use at the time.) The first random newspaper that I pulled up from a search for "london newspaper 1950" has sermon references on the front page so it seems like an important missing piece.
Somewhat missing the cutoff of 1875 is the revised NT of the KJV. Work on it started in 1870 but likely wasn't used widely before 1881.
The problem is that the 'genius' of Einstein wasn't just synthesizing existing data,but actively rejecting the axioms of that data. The 1875 corpus overwhelmingly 'proves' absolute time and the luminiferous aether. A model optimizing for the most probable continuation will converge on that consensus.
To get Relativity, the model needs to realize the training data isn't just incomplete, but fundamentally wrong. That requires abductive reasoning (the spark of genius) to jump out of the local minimum. Without that AGI-level spark, a 'pure knowledge pile' will just generate a very eloquent, mathematically rigorous defense of Newtonian physics.
There was a discussion around a very similar model (Qwen3 based) some weeks ago:
https://news.ycombinator.com/item?id=46319826
I found it particularly thought-inspiring how a model with training from that time period completely lacks context/understanding of what it is itself, but then I realized that we are the same (at least for now).
v0: 16M Parameters
v0.5 123M Parameters
v1: 700M Parameters
v2mini-eval1: 300M Parameters
I would not call this LLM. This is not large. It's just a normal-sized LM. Or even small.
(It's also not a small LLM.)
Oh I have really been thinking long about this. The intelligence that we have in these models represent a time.
Now if I train a foundation models with docs from library of Alexandria and only those texts of that period, I would have a chance to get a rudimentary insight on what the world was like at that time.
And maybe time shift further more.
> I would have a chance to get a rudimentary insight on what the world was like at that time
Congratulations, you've reinvented the history book (just with more energy consumption and less guarantee of accuracy)
History books, especially those from classical antiquity, are notoriously not guaranteed to be accurate either.
Do you expect something exclusively trained on them to be any better?
To a large extent, yes. A model trained on many different accounts of an event is likely going to give a more faithful picture of that event than any one author.
This isn't super relevant to us because very few histories from this era survived, but presumably there was sufficient material in the Library of Alexandria to cover events from multiple angles and "zero out" the different personal/political/religious biases coloring the individual accounts.
Is there a link where I can try it out?
Edit: I figured it out
"The Lord of the Rings uding the army under the command of his brother, the Duke of York, and the Duke of Richmond, who fell in the battle on the 7th of April, 1794. The Duke of Ormond had been appointed to the command of the siege of St. Mark's, and had received the victory of the Rings, and was thus commanded to move with his army to the relief of Shenham. The Duke of Ormond was at length despatched to oppose them, and the Duke of Ormond was ordered
Fun idea, but all of the output they demo over the course of the various versions is unusable. You can see progress clearly being made though - maybe v3 will pass muster.
This kind of technique seems like a good way to test model performance against benchmarks. I'm too skeptical that new models are taking popular benchmark solutions into their training data. So-- how does e.g. ChatGPT's underlying architecture perform on SWE-bench if trained only on data prior to 2024.
> are taking popular benchmark solutions into their training data
That happened in the past, and the "naive" way of doing it is usually easy to spot. There are, however, many ways in which testing data can leak into models, even without data contamination. However this doesn't matter much, as any model that only does well in benchmarks but is bad in real-world usage will be quickly sussed out by people actually using them. There are also lots and lots of weird, not very popular benchmarks out there, and the outliers are quickly identified.
> perform on SWE-bench if trained only on data prior to 2024.
There's a benchmark called swe-REbench, that takes issues from real-world repos, published ~ monthly. They perform tests and you can select the period and check their performance. This is fool-proof for open models, but a bit unknown for API-based models.
It would be interesting if there's enough data to train a model capable enough to converse with and ask about contemporary views on issues of the day, or what it thought about "potential" future events/technologies yet to happen.
LOL
PROMPT:Charles Darwin
Charles DarwinECCEMACY. Sir, — The following case is interesting to me : — I was in London a fortnight, and was much affected with an attack of rheumatism. The first attack of rheumatism was a week before I saw you, and the second when I saw you, and the third when I saw you, and the third in the same time. The second attack of gout, however, was not accompanied by any febrile symptoms, but was accompanied with an increased flow of urine, and with a more copious discharge of urine. The third attack was an hour after I saw you, and was succeeded by a return of a paroxysm of gout, and a more rapid return of the gout. The fourth attack was also accompanied by a fever, but was not always accompanied by any febrile symptoms. The third attack of gout was a fortnight after you had been ill, and the fourth was followed by a paroxysm of gout. The fourth attack was a fortnight after you were attacked, and was accompanied by a sense
Interesting that it reads a bit like it came from a Markov chain rather than an LLM. Perhaps limited training data?
Can you confidently say that the architure of the LLM doesn't include any a priori bias that might effect the integrity of this LLM?
That is, the architectures of today are chosen to yield the best results given the textual data around today and the problems we want to solve today.
I'd argue that this lack of bias would need to be researched (if it hasn't been already) before this kind of model has credence.
LLMs aren't my area of expertise but during my PhD we were able to encode a lot of a priori knowledge through the design of neural network architectures.
A LLM trained only on data from certain time periods to ~reduce modern bias~ enhance past bias
Doesn’t that seem useful though? Isn’t that why I’m forced to read “This movie was made a time when racial stereotypes were different and not well considered” or whatever on old movies?
I think talking to a legit trained LLM from a different era would be rad. But… this seems the opposite of Gemini making black lady popes and Native American Nazis… that these views wouldn’t really be “allowed” (published by anyone that wants AI funding money).
I would pay like $200/month if there was an LLM out there that I could only communicate with using an old-timey telegraph key and morse code.
Let's see how someone from the past reacts when you tell them about modern technology
Can I use it to get up-to-date legal advice on Arizona reproductive health laws?
Would be interesting to train a cutting edge model with a cut off date of say 1900 and then prompt it about QM and relativity with some added context.
If the model comes up with anything even remotely correct it would be quite a strong evidence that LLMs are a path to something bigger if not then I think it is time to go back to the drawing board.