To clear up a few misconceptions:
- Aristotle uses modern AI techniques heavily, including language modeling.
- Aristotle can be guided by an informal (English) proof. If the proof is correct, Aristotle has a good chance at translating it into Lean (which is a strong vote of confidence that your English proof is solid). I believe that's what happened here.
- Once a proof is formalized into Lean (assuming you have formalized the statement correctly), there is no doubt that the proof is correct. This is the core of our approach: you can do a lot of (AI-driven) search, and once you find the answer you are certain it's correct no matter how complex the solution is.
Happy to answer any questions!
The natural-language statement of the problem is (from https://www.erdosproblems.com/728):
> Let C>0 and ϵ>0 be sufficiently small. Are there infinitely many integers a,b,n with a≥ϵn and b≥ϵn such that a!b!∣n!(a+b−n)! and a+b>n+Clogn?
The Lean-language statement of the problem (which can be done either by hand or by AI) is (from https://github.com/plby/lean-proofs/blob/f44d8c0e433ab285541...):
∀ᶠ ε : ℝ in [>] 0, ∀ C > (0 : ℝ), ∀ C' > C,
∃ a b n : ℕ,
0 < n ∧
ε * n < a ∧
ε * n < b ∧
a ! * b ! ∣ n ! * (a + b - n)! ∧
a + b > n + C * log n ∧
a + b < n + C' * log n
(There's also an older formulation at https://github.com/google-deepmind/formal-conjectures/blob/f... but the new one is more in the spirit of what was intended: see the discussion starting at https://www.erdosproblems.com/forum/thread/728#post-2196 which gives a clear picture, as of course does Tao's thread in the OP that summarizes this discussion.)
Given a formal statement of what you want, Lean can validate that the steps in a (tedious) machine-readable purported proof are valid and imply the result from accepted axioms. This is not AI, but a tiny, well reviewed kernel that only accepts correct formal logic arguments.
So, if you have a formal statement that you've verified to represent what you are interested in by some other means, Lean can tell you whether the proof created by genAI is correct. Basically, there is a nigh infallible checker that won't accept incorrect hallucinations.
> Let C>0 and ϵ>0 be sufficiently small. Are there infinitely many integers a,b,n with a≥ϵn and b≥ϵn such that a!b!∣n!(a+b−n)! and a+b>n+Clogn?
Which seems like it's the type of thing you give as a homework problem to state formally in an intro class.
Let's just be generous and try to accept these differences.
I can't speak for ndriscoll, but I am a university math professor with extensive experience teaching these sorts of topics, and I agree with their comment in full.
You are right that some (other) statements are harder to formalize than they look. The Four Color Theorem from graph theory is an example. Generally speaking, discrete math, inequalities, for all/there exists, etc. are all easy to formalize. Anything involving geometry or topology is liable to be harder. For example, the Jordan curve theorem states that "any plane simple closed curve divides the plane into two regions, the interior and the exterior". As anyone who has slogged through an intro topology book knows, statements like this take more work to make precise (and still more to prove).
When someone takes the time to explain undergrad-level concepts in a comment, responding with "are you an expert?" is a level of skepticism that's bordering on hostile. The person you're responding to is correct, it's rare that the theorem statement itself is particularly hard to formalize. Whatever you read likely refers to the difficulty of formalizing a proof.
That's very dependent on the problem area. For example there's a gap between high school explanation of central limit theorem and actual formalization of it. And when dealing with turing machines sometimes you'll say that something grows e.g. Omega(n), but what happens is that there's some subsequence of inputs for which it does. Generally for complexity theory plain-language explanations can be very vague, because of how insensitive the theory is to small changes and you need to operate on a higher level of abstraction to have a chance to explain a proof in reasonable time.
On the other hand, many problems in number theory and discrete structures tend to be rather simple to formalize. If you want to take your own look at that, I'd recommend to check out the lean games[1]. I'd say after the natural numbers game, you most likely know enough lean to write that problem down in lean with the "sufficiently small" being the trickiest part.
The correct question would have been, does anyone else agree with the statement.
In this particular case, the amount knowledge needed (of e.g. Lean language, math and Erdos problems) means any credible statement about the difficulty requires an expert.
These are the two main problems:
1. Formalizing a theorem.
2. Finding a formal proof.
Part 2 is where AI could help as proof search is full of heuristics. That's also how humans find proofs and is one of the main skills of a mathematician. The formal proof can then be machine checked with well known and mature techniques not involving AI.
Part 1 is the part that's missing and will always be hard. It's also the issue with formal verification of programs for which correctness criteria are often very complex and it's easy to mess up the formalization, so that even if you trust the proof, you can't trust that it proves the right thing.
Also, on the verification side - there could also be a window of failure that Lean itself has a hidden bug in it too. And with automated systems that seek correctness, it is slightly elevated that some missed crack of a bug becomes exploited in the dev-check-dev loop run by the AI.
The lean proof checker then checks to make sure the proof actually proves the statement.
In this case an AI is generating the proof, but if it "compiles" it's correct. The only thing humans need to check is the statement to be proven.
(I don't know anything about this project but I've played around with lean and used other proof checkers more sesrisously).
It seems one side of this argument desperately needs AI to have failed, and the other side is just saying that it probably worked but it is not as important as presented, that it is actually just a very cool working methodology going forward.
Sure. But it's fair to ask how to validate that assumption.
If it is easier to convert backwards, maybe the AI can at least describe what the equations mean…
A little bit nitpicking, but according to books like AIMA that is indeed AI. In the first chapter even any control system is classified as AI.
Because of the reasons stated in the 1st chapter, I totally agree with the authors.
The whole system is AI. That part is a verifier in a chain of “suggestions/instict -> verifier” like used in neurosymbolic systems for automated driving, for example.
1. you write a proof in English that there is an infinite number of primes. 2. the llm writes 2+2=4 in lean. 3. lean confirms that this is correct and it's impossible that this proof is wrong.
The question is in the person (or AI) creating the formal problem statement - how do you know it represents the problem the proof is supposed to be for? And the answer is for people in the field, in this case, formalizing the problem and verifying the formalization is easy. It is like generating an public key versus factoring it.
Aristotle is doing the matching AI part, the modern LLM approach, previously called fuzzy logic.
Both are AI.
To me "AI" is machine learning, statistical algorithms trained on data. That's not true for Lean.
However, there are some good heuristics. If you expect a problem to be hard and the proof is very short, you've probably missed something!
This is also a big risk when trying to prove code correctness: "prove this algo works" means you gotta define "works" along certain axes, and if you're very unlucky you might have a proof that exploits the uncertainty around a certain axis.
My limited understanding of Rust is that it applies a fixed set of rules to guarantee memory safety. The rules are somewhat simple and limiting, for ease of understanding and implementation, but also because of undecidability.
Programmers run into situations where they know that their code won't cause memory errors, but it doesn't follow the rules. Wouldn't it be cool if something like Aristotle was integrated into the compiler? Any code for which a proof of correctness could be written would pass/compile, without having to add more and more rules
Formal methods are cool because, by contrast to tools like the borrow checker, you can prove some very "nonlocal" properties: this system does not deadlock, or it makes progress at least every N steps, etc.
For example can it read rust async code and prove that there are no deadlocks in TLA+, or some equivalent in Lean?
If the proof is rooted in the programmer's understanding who can give proof hints to the prover then any modification of the program can then be accompanied with a modification of the hints, still allowing automatic proofs. But if the human has no clue then the automatic system can get stuck without the human having a chance to help it along.
Eg you can give the vague spec 'build me a todo list app', but you can still formally prove that everything your app does finishes, or even that it finishes in reasonable time.
Now this captures some errors, but it doesn't really capture high level ones (is this program guaranteed to not deadlock is a hard one), and it doesn't capture the one that is important for business purposes (does this do what the customer wants). That requirement is more important than correctness (vitness all the software that is described as "crap", but is nonetheless widely used).
I don't think this is a required key to unlocking vibe coding. That seems to be easy: does this provide business value? And there the answer seems roughly to be "yes".
Sometimes when I'm using new LLMs I'm not sure if it’s a step forward or just benchmark hacking, but formalized math results always show that the progress is real and huge.
When do you think Harmonic will reach formalizing most (even hard) human written math?
I saw an interview with Christian Szegedy (your competitor I guess) that he believes it will be this year.
In other fields (topology, probability, linear algebra), many key definitions are not in Mathlib yet, so you will struggle to write down the theorem itself. (But in some cases, Aristotle can define the structure you are talking about on the fly!)
This is not an intrinsic limitations of Lean, it's just that nobody has taken the time to formalize much of those fields yet. We hope to dramatically accelerate this process by making it trivial to prove lemmas, which make up much of the work. For now, I still think humans should write the key definitions and statements of "central theorems" in a field, to ensure they are compatible with the rest of the library.
Do you have any plans to characterize these cases more fully, and perhaps propose your own contributions to mathlib itself on that basis?
How does this depend on the area of mathematics of the proof? I was under the impression that it was still difficult to formalize most research areas, even for a human. How close is Aristotle to this frontier?
That's a pretty big assumption, though, isn't it? As we saw the Navier-Stokes psychosis episode over the New Year holiday, formalizing correctly really isn't guaranteed.
It's another reformulation rather than a true proof. Now, instead of wanting a proof of a theorem, now we just need to prove that this proof is actually proving the theorem. The proof itself being so incomprehensible that it can't on its own be trusted, but if it can be shown that it can be trusted then the theorem must be true.
Claude Code can write lean, but we do a heck of a lot of RL on theorem proving, so Aristotle winds up being much better at writing Lean than other coding agents are.
Have you tried Aristotle on other, non-Lean tasks? Is it better at logical reasoning in general?
Do you have any links to reading about how often lean core has soundness bugs or mathlib has correctness bugs?
Second, when you say language modeling support, it means that can better understand code representation (ASTs) or something else? I am just an AI user, not very knowledgeable in the field. My main interest is if it would be great for static analysis oriented to computer security (SAST).
I don’t think it can really be said to have occurred autonomously then?
Looks more like a 50/50 partnership with a super expert human one the one side which makes this way more vague in my opinion - and in line with my own AI tests, ie. they are pretty stupid even OPUS 4.5 or whatever unless you're already an expert and is doing boilerplate.
EDIT: I can see the title has been fixed now from solved to "more or less solved" which is still think is a big stretch.
Seems exactly like the tests at my company where even frontier models are revealed to be very expensive rubber ducks, but completely fails with non experts or anything novel or math heavy.
Ie. they mirror the intellect of the user but give you big dopamine hits that'll lead you astray.
But speaking as a specialist in theorem proving, this result is pretty impressive! It would have likely taken me a lot longer to formalize this result even if it was in my area of specialty.
How did you arrive at "ridiculous"? What we're seeing here is incredible progress over what we had a year ago. Even ARC-AGI-2 is now at over 50%. Given that this sort of process is also being applied to AI development itself, it's really not clear to me that humans would be a valuable component in knowledge work for much longer.
To me that doesn't sound qualitatively different from a PhD student. Are they just cognitive augmentation for their mentor?
In any case, I wasn't trying to argue that this system as-is is AGI, but just that it's no longer "ridiculous", and that this to me looks like a herald of AGI, as the portion being done by humans gets smaller and smaller
>The real world is not composed of rewards and punishments.
Most "AGI advocates" say that AGI is coming, sooner rather than later, and it will fundamentally reshape our world. On its own that's purely descriptive. In my experience, most of the alleged "smiting" comes from the skeptics simply being wrong about this. Rarely there's talk of explicit rewards and punishments.
To me, this sounds like when we first went to the moon, and people were sure we'd be on Mars be the end of the 80's.
> Even ARC-AGI-2 is now at over 50%.
Any measure of "are we close to AGI" is as scientifically meaningful as "are we close to a warp drive" because all anyone has to go on at this point is pure speculation. In my opinion, we should all strive to be better scientists and think more carefully about what an observation is supposed to mean before we tout it as evidence. Despite the name, there is no evidence that ARC-AGI tests for AGI.
Unlike space colonisation, there are immediate economic rewards from producing even modest improvements in AI models. As such, we should expect much faster progress in AI than space colonisation.
But it could still turn out the same way, for all we know. I just think that's unlikely.
Ai runs on computers. Consider the undecidability of Rices theorem. Where compiled code of non trivial statements may or may not be error free. Even an ai can’t guarantee its compiled code is error free. Not because it wouldn’t write sufficient code that solves a problem, but the code it writes is bounded by other externalities. Undecidability in general makes the dream of generative ai considerably more challenging than how it’s being ‘sold.
I work at an insurance company and I can’t see AI replacing even 10% of the employees here. Too much of what we do is locked up in decades-old proprietary databases that cannot be replaced for legal reasons. We still rely on paper mail for a huge amount of communication with policyholders. The decisions we make on a daily basis can’t be trusted to AI for legal reasons. If AI caused even a 1% increase in false rejections of claims it would be an enormous liability issue.
And regarding your example of an insurance company, I'm not sure about that industry, but seeing the transformation of banking over the last decade to fully digital providers like Revolut, I would expect similar disruption there.
Recent case:
I have a bar with a number of weights supported on either end:
|---+-+-//-+-+---|
What order and/or arrangement or of removing the weights would cause the least shift in center-of-mass? There is a non-obvious trick that you can pull here to reduce the shift considerably and I was curious if the AI would spot it or not but even after lots of prompting it just circled around the obvious solutions rather than to make a leap outside of that box and come up with a solution that is better in every case.
I wonder what the cause of that kind of blindness is.
My guess is: first move the weights to the middle, and only then remove them.
However “weights” and “bar” might confuse both machines and people into thinking that this is related to weight lifting, where there’s two stops on the bar preventing the weights from being moved to the middle.
And no, the problem is not 'not clearly stated'. It is complete as it is and you are wrong about your guess.
And if machines and people think this is related to weight lifting then they're free to ask follow up questions. But even in the weight lifting case the answer is the same.
Yeah, LLMs have a tendency to run with some interpretation of a question without asking follow-up questions. Probably, it's a consequence of RLHFing them in that way.
If you do solve it don't post the answer.
You didn't even label your ASCII art, so I've no clue what you mean, are the bars at the end the supports or weights? Can I only remove one weight at a time? Initially I assumed you mean a weightlifting bar the weights on which can only be removed from its ends. Is that the case or what? What's the double slash in the middle?
Also: "what order and/or arrangement or of removing the weights" this isn't even correct English. Arrangement of removing the weights? State the problem clearly, from first principles, like you were talking to a 5 year old.
The sibling comment is correct, you're clearly picturing something in your mind that you're failing to properly describe. It seems obvious to you, but it's not.
There is a starting node (L_0, R_0, {}) and an ending node ({}, {}, W) , with the latter having L=R={}.
I think you're trying to find the path (L_n, R_n, S_n) from the starting node to the ending node that minimises the maximum absolute value of c(L_n, R_n, S_n).
I won't post a solution, as requested.
"Aristotle integrates three main components: a Lean proof search system, an informal reasoning system that generates and formalizes lemmas, and a dedicated geometry solver"
Not saying it's not an amazing setup, i just don't understand the word "AI" being used like this when it's the setup / system that's brilliant in conjunction with absolute experts.
https://en.wikipedia.org/wiki/Dartmouth_workshop
AI != AGI != neural networks != LLMs
But Tao did mention ChatGPT so i believe LLMs were involved at least partially.
This hits so true to home. Just today in my field a manager without expertise in a topic gave me an AI solution to something I am an expertise in. The AI was very plainly and painfully wrong, but it comes down to the user prompting really poorly. When I gave a el formulated prompt to the same topic, I got the correct answer on the first go.
> There seems to be some confusion on this so let me clear this up. No, after the model gave its original response, I then proceeded to ask it if it could solve the problem with C=k/logN arbitrarily large. It then identified for itself what both I and Tao noticed about it throwing away k!, and subsequently repaired its proof. I did not need to provide that observation.
so it was literally "yo, your proof is weak!" - "naah, watch this! [proceeds to give full proof all on its own]"
I'd say that counts
"solved more or less autonomously by AI" were Tao's exact words, so I think we can trust his judgment about how much work he or the AI did, and how this indicates a meaningful increase in capabilities.
Or someone like Terence Tao might figure out how to wield AI better than anyone else, even the labs, and use the tools to take a bunch down at once. I'm excited to see what's coming this year.
To a layman, that doesn't sound like very AI-like? Surely there must be a dozen algorithms to effectively search this space already, given that mathematics is pretty logical?
Isabelle has had the "Sledgehammer" tool for quite awhile [1]. It uses solvers like z3 to search and apply a catalog of proof strategies and then try and construct a proof for your main proof or any remaining subtasks that you have to complete. It's not perfect but it's remarkably useful (even if it does sometimes give you proofs that import like ten different libraries and are hard to read).
I think Coq has Coqhammer but I haven't played with that one yet.
Sorry for my probably senseless questions, as I'm trying to map the computing model of math solvers to common PL semantics. Probably there is better overview literature. I'd like to get an overview of proof system runtime semantics for later usage. 2 Is there an equivalent of fuzz testing (of computing systems) in math, say to construct the general proof framework? 3 Or how are proof frameworks (based on ideas how the proof could work) constructed? 4 Do I understand it correct, that math in proof systems works with term rewrite systems + used theory/logic as computing model of valid representation and operations? How is then the step semantic formally defined?
2. there is no exact analogue of fuzz-testing bytecode for "theorem fuzzing", but arguably the closest match is counterexample generators - model finders, finite-model search, SMT instantiation with concrete valuations, all serve a role similar to fuzzers by finding invalid conjectures quickly.
these are weaker than fuzzing for finding execution bugs because mathematical statements are higher-level and provers operate symbolically.
3. here's how proof frameworks are constructed, at the high-level:
a. start by picking a logical foundation: e.g., first-order logic (FOL), higher-order logic (HOL), dependent type theory (Martin-Löf/CIC).
b. define syntax (terms, types, formulas) and typing/judgment rules (inference rules or typing rules).
c. define proof objects or proof rules: natural deduction, sequent calculus, Hilbert-style axioms, or type-as-proofs (Curry-Howard).
d. pick or design the kernel: small, trusted inference engine that checks proof objects (reduction rules, conversion, rule admissibility).
e. add automation layers: tactics, decision procedures, external ATP/SMT, rewriting engines.
f. provide libraries of axioms/definitions and extraction/interpretation mechanisms (code extraction, models).
g. implement proof search strategies and heuristics (backtracking, heuristics, lemma databases, proof-producing solvers).
this is the standard engineering pipeline behind modern ITPs and automated systems.
4. yes, many proof assistants and automated provers treat computation inside proofs as:
a. term rewriting / reduction (beta-reduction, delta-unfolding, normalization) for computation oriented parts; this is the "computation" layer.
b. a separate deductive/logic layer for reasoning (inference rules, quantifier instantiation, congruence closure).
the combined model is: terms represent data/computation; rewriting gives deterministic computation semantics; logical rules govern valid inference about those terms. Dependent type theories conflate computation and proof via conversion (definitional equality) in the kernel.
5. here's how a proof step's semantics is defined:
proof steps are applications of inference rules transforming sequents/judgments. formally:
a. a judgment form J (e.g., Γ ⊢ t : T or Γ ⊢ φ) is defined.
b. inference rules are of the form: from premises J1,...,Jn infer J, written as (J1 ... Jn) / J.
c. a proof is a finite tree whose nodes are judgments; leaves are axioms or assumptions; each internal node follows an inference rule.
d. for systems with computation, a reduction relation → on terms defines definitional equality; many rules use conversion: if t →* t' and t' has form required by rule, the rule applies.
e. in type-theoretic kernels, the step semantics is checking that a proof object reduces/normalizes and that constructors/eliminators are used respecting typing/conversion; the kernel checks a small set of primitive steps (e.g., beta, iota reductions, rule applications).
operationally: a single proof step = instantiate a rule schema with substitutions for metavariables, perform any required reductions/conversions, and check side conditions (freshness, well-formedness).
equational reasoning and rewriting: a rewrite rule l → r can be applied to a term t at a position p if the subterm at p matches l under some substitution σ; result is t with that subterm replaced by σ(r). Confluence/termination properties determine global behavior.
higher-level tactics encode sequences of such primitive steps (rule applications + rewrites + searches) but are not part of kernel semantics; only the kernel rules determine validity.
relevant concise implications for mapping to PL semantics:
treat proof state as a machine state (context Γ, goal G, local proof term), tactics as programs that transform state; kernel inference rules are the atomic instruction set; rewriting/normalization are deterministic evaluation semantics used inside checks.
automation ≈ search processes over nondeterministic tactic/program choices; model finders/SMTs act as external oracles producing either counterexamples (concrete) or proof certificates (symbolic).
1. Yes, the Sledgehammer suite contains three testing systems that I believe are concolic (quickcheck, nitpick, nunchaku), but they only work due to hand-coded support for the mathematical constructs in question. They'd be really inefficient for a formalised software environment, because they'd be operating at a much lower-level than the abstractions of the software environment, unless dedicated support for that software environment were provided.
2. Quickcheck is a fuzz-testing framework, but it doesn't help to construct anything (except as far as it constructs examples / counterexamples). Are you thinking of something that automatically finds and defines intermediary lemmas for arbitrary areas of mathematics? Because I'm not aware of any particular work in that direction: if computers could do that, there'd be little need for mathematicians.
3. By thinking really hard, then writing down the mathematics. Same way you write computer programs, really, except there's a lot more architectural work. (Most of the time, the computer can brute-force a proof for you, so you need only choose appropriate intermediary lemmas.)
4. I can't parse the question, but I suspect you're thinking of the meta-logic / object-logic distinction. The actual steps in the term rewriting are not represented in the object logic: the meta-logic simply asserts that it's valid to perform these steps. (Not even that: it just does them, accountable to none other than itself.) Isabelle's meta-logic is software, written in a programming language called ML.
Logic solvers are useful, but not tractable as a general way to approach mathematics.
To be clear, there are explicitly computationally tractable fragments of existing logics, but they're more-or-less uninteresting by definition: they often look like very simple taxonomies (i.e. purely implicational) or like a variety of "modal" and/or "multi-modal" constructions over simpler logics.
Of course it would be nice to explicitly tease out and write down the "computationally tractable" general logical reasoning that some existing style of proof is implicitly relying on (AIUI this kind of inquiry would generally be comprised under "synthetic mathematics", trying to find simple treatments in axiom- and rule-of-inference style for existing complex theories) but that's also difficult.
The fact of how you use the term AI tells me that you are a representative of laymen so what precisely are you trying to define?
It might be helpful to understand the term artificial intelligence first:
For all topics that can be expressed with language, the value of LLMs is shuffling things around to tease out a different perspective from the humans reading the output. This is the only realistic way to understand AI enough to make it practical and see it gain traction.
As much as I respect Tao, I feel like his comments about AI usage can be misleading without carefully reading what he is saying in the linked posts.
A large amount of Tao's work is around using AI to assist in creating Lean proofs.
I'm generally on the more skeptical side of things regarding LLMs and grand visions, but assisting in the creation of Lean proofs is a huge area of opportunity for LLMs and really could change mathematics in fundamental ways.
One naive belief many people have is that proofs should be "intelligible" but it's increasingly clear this is not the case. We have proofs that are gigabytes (I believe even terabytes in some cases) in size, but we know they are correct because they check in Lean.
This particular pattern of using state of the art work in two different areas (LLMs and theorem proving) absolutely has the potentially to fundamentally change how mathematics is done. There's a great picture on pp 381 of Type Theory and Formal Proof where you can easily see how LLMs can be placed in two of the most tricky parts of that diagram to solve.
Because the work is formally verified we can throw out entire classes of LLM problems (like hallucinations).
Personally I think strongly typed language, with powerful type systems are also the long term ideal coding with LLMs (but I'm less optimistic about devs following this path).
Perhaps you meant, "a decent amount of his recent work." He has been doing math long before LLMs, and is still regularly publishing papers with collaborators that have nothing to do with AI. The most recent was last week. https://arxiv.org/search/math?searchtype=author&query=Tao,+T
It absolutely is. With the twist that ChatGPT 5.2 can now also "explain" an AI-generated Lean proof in human-readable terms. This is a game changer, because "refactoring" can now become end-to-end: if the human explanation of a Lean proof is hard to grok and could be improved, you can test changes directly on the formal text and check that the proof still goes through for the original statement.
Formal verification combined with AI is, imho, exactly the type of thinking that gets the most value out of the current state of LLMs.
https://www.science.org/cms/asset/7f2147df-b2f1-4748-9e98-1a...
That’s one of the main reason why I did not pursue an academic math career. The pure joy of solving exam problems with elegant proofs is very hard to get on harder problems.
That's not a naïve belief. Intelligible proofs represent insight that can be applied to other problems. If our only proof is an opaque one, that means we don't really understand the area yet. Take, for example, the classification of finite simple groups (a ten-thousand-page proof): that is very much not a closed area of research, and we're still discovering new things in the vicinity of the problem.
What happens if we reason upwards but change some universal constants? What happens if we use Tao instead of Pi everywhere, these kind of fun questions would otherwise require an enormous intellectual effort whereas with the mechanisation and automation of thought, we might be able to run them and see!
Google Scholar was a huge step forward for doing meta-analysis vs a physical library.
But agents scanning the vastness of PDFs to find correlations and insights that are far beyond human context-capacity will I hope find a lot of knowledge that we have technically already collected, but remain ignorant of.
In any given scientific niche, there is a huge amount of tribal knowledge that never gets written down anywhere, just passed on from one grad student to the rest of the group, and from there spreads by percolation in the tiny niche. And papers are never honest about the performance of the results and what does not work, there is always cherry picking of benchmarks/comparisons etc.
There is absolutely no way you can get these kinds of insights beyond human context capacity that you speak of. The information necessary does not exist in any dataset available to the LLM.
Imagine trying to teach an AI how to code based on only slide decks from consultants. No access todocumentation, no stack overflow, no open source code used in the training data; just sales pitches and success stories. That's close to how absurd this idea is.
It is likely we might see some AlphaGo type new styles in existing research workflows that AI might work out if there is some verification logic. Humans could probably never go into that space, or may be none of the researchers ever ventured there due to different reasons as progress in general is mostly always incremental.
Literally nothing other than mild convenience. It’s just 2pi.
I'm not sure why that point gets lost in these discussions. And personally, I think of the set of fundamental mathematical objects as having a unique and objective definition. So, I get weirdly bothered by the offset in the Gamma function.
The "intellectual effort" this requires is about 0.
Maybe you meant Euler's number? Since it also relates to PI, it can be used and might actually change the framework in an "interesting way" (making it more awkward in most cases - people picked PI for a reason).
I think what they were getting at is something like this: The application of existing ideas that simply haven't been applied in certain ways because it's too boring or obvious or abstract for humans to have bothered with, but AI can plow through a year's worth of human drudgery in a day or a month or so, and that sort of "brute force" won't require any amazing new technical capabilities from AI.
https://ddcolrs.wordpress.com/2018/01/17/maxwells-equations-...
"I doubt that anything resembling genuine "artificial general intelligence" is within reach of current #AI tools. However, I think a weaker, but still quite valuable, type of "artificial general cleverness" is becoming a reality in various ways.
By "general cleverness", I mean the ability to solve broad classes of complex problems via somewhat ad hoc means. These means may be stochastic or the result of brute force computation; they may be ungrounded or fallible; and they may be either uninterpretable, or traceable back to similar tricks found in an AI's training data. So they would not qualify as the result of any true "intelligence". And yet, they can have a non-trivial success rate at achieving an increasingly wide spectrum of tasks, particularly when coupled with stringent verification procedures to filter out incorrect or unpromising approaches, at scales beyond what individual humans could achieve.
This results in the somewhat unintuitive combination of a technology that can be very useful and impressive, while simultaneously being fundamentally unsatisfying and disappointing - somewhat akin to how one's awe at an amazingly clever magic trick can dissipate (or transform to technical respect) once one learns how the trick was performed.
But perhaps this can be resolved by the realization that while cleverness and intelligence are somewhat correlated traits for humans, they are much more decoupled for AI tools (which are often optimized for cleverness), and viewing the current generation of such tools primarily as a stochastic generator of sometimes clever - and often useful - thoughts and outputs may be a more productive perspective when trying to use them to solve difficult problems."
This comment was made on Dec. 15, so I'm not entirely confident he still holds it?
While quickly I noticed that my pre-ChatGPT-3.5 use of the term was satisfied by ChatGPT-3.5, this turned out to be completely useless for 99% of discussions, as everyone turned out to have different boolean cut-offs for not only the generality, but also the artificiality and the intelligence, and also what counts as "intelligence" in the first place.
That everyone can pick a different boolean cut-off for each initial, means they're not really booleans.
Therefore, consider that this can't drive a car, so it's not fully general. And even those AI which can drive a car, can't do so in genuinely all conditions expected of a human, just most of them. Stuff like that.
So blind people are not general intelligences?
So no we do not deem a blind person to be unintelligent due to their lack of being able to drive without sight. But we might judge a sighted person as being not generally intelligent if they could not drive with sight.
AI can already beat humans in pretty much any game like Go or Chess or many videogames, but that doesn't make it general.
Just because a specialized human placed in an F-16 can fly at Mach 2.0, doesn't mean humans in general can fly.
What happens when we put an artificial general intelligence in an F-16? That's what happened here with this proof.
I assumed extreme performance of a general AI matching and exceeding average human intelligence when placed in an F16 or an equivalent cockpit specified for conducting math proofs.
That’s not agi at all. I don’t think you understand that LLMs will never hit agi even when they exceed human intelligence in all applicable domains.
The main reason is they don’t feel emotions. Even if the definition of agi doesn’t currently encompass emotions people like you will move the goal posts and shift the definition until it does. So as AI improves, the threshold will be adjusted to make sure they will never reach agi as it’s an existential and identity crisis to many people to admit that an AI is better than them on all counts.
Are you getting the same value in your work, in your field?
For me, deep research tools have been essential for getting caught up with a quick lit review about research ideas I have now that I'm transitioning fields. They have also been quite helpful with some routine math that I'm not as familiar with but is relatively established (like standard random matrix theory results from ~5 years ago).
It does feel like the spectrum of utility is pretty aligned with what you might expect: routine programming > applied ML research > stats/applied math research > pure math research.
I will say ~1 year ago they were still useless for my math research area, but things have been changing quickly.
So yes, there is value here, and quite a bit but it requires a lot of forethought in how you structure your prompts and you need to be super skeptical about the output as well as able to check that output minutely.
If you would just plug in a bunch of data and formulate a query and would then use the answer in an uncritical way you're setting yourself up for a world of hurt and lost time by the time you realize you've been building your castle on quicksand.
I also found it can be helpful when exploring your mathematical intuitions on something, e.g. like how a dropout layer might effect learned weights and matrix properties, etc. Sometimes it will find some obscure rigorous math that can be very enlightening or relevant to correcting clumsy intuitions.
Overall: useful, but not yet particularly "accelerating" for me.
But maybe that is just me. I have read some of Terence Tao's transcripts, and the questions he asks LLMs are higher complexity than what I ask. Yet, he often gets reasonable answers. I don't yet know how I can get these tools to do better.
The difference between free ChatGPT, GPT-5.2 Thinking, and GPT-5.2 Pro is enormous for areas like logic and math. Often the answer to bad results is just to use a better model.
Additionally, sometimes when I get bad results I just ask the question again with a slightly rephrased prompt. Often this is enough to nudge the models in the right direction (and perhaps get a luckier response in the process). However, if you are just looking at a link to a chat transcript, this may not be clear.
I have wondered if he has access to a better model than I, the way some people get promotional merchandise. A year or two ago he was saying the models were as good as an average math grad student when to me they were like a bad undergrad. In the current models I don't get solutions to new problems. I guess we could do some debugging and try prompting our models with this Erdos problem and see how far we get. (edit: Or maybe not; I guess LLMs search the web now.)
Not trivial problems. Issues with possible solutions, errors, and unresolved history.
AI did not \\"solve\\" any issues on its own, but what stood out to me was the speed at which concepts could be rewritten, restructured and tested for stress.
A mental model that has been useful to me is that AI is not particularly good at providing the first answer, however, it is very good at providing the second, third, and tenth versions of the answer, especially when the first answer has already been identified as weak by a human.
In these instances, the progress seemed to stem from the AI being able to: Quickly reword and restate a given argument. Convert implicit assumptions into explicit ones. Identify small gaps in logic before they became large.
What I have been grappling with is how to differentiate when AI is just clarifying versus when it is silently hallucinating structure. Is the output of AI being treated as a draft, a reviewer, a rubber duck, or some combination? When is the output so fast that the rigor of thought is compromised? I am interested in how others are using AI for hard thinking and not just for writing cleanup.
- It took me a minute or two of clicking around to figure out that the (only?) way to use it is to create an API key, then start aristotle in the terminal and interact with it there. It could be more obvious I think.
- Your profile links to http://www.cs.stanford.edu/~tachim/ which doesn't work; should be http://cs.stanford.edu/~tachim/ (without the www) (I think Stanford broke something recently for the former not to work.)
Edit: I just realized from https://news.ycombinator.com/item?id=46296801 that you're the CEO! - in that case maybe you, or whoever you think most appropriate from your organization, could submit it along with a text description of what it is, and what is the easiest and/or most fun way to try it out?
Edit: Also https://hn.algolia.com/?dateRange=all&page=2&prefix=true&que... for the most popular Show HNs. Don't be discouraged we like personal/open source projects most often, Obsidian made #1
EDIT: Look at all the people below just reacting to the headline and clearly not reading the posts. Aristotle (https://arxiv.org/abs/2510.01346) is key here folks.
EDIT2: It is clear much of the people below don't even understand basic terminology. Something being a transformer doesn't make it an LLM (vision transformers, anyone) and if you aren't training on language (e.g. AlphaFold, or Aristotle on LEAN stuff), it isn't a "language" model.
I think it's because it comes off as you are saying that we should move off of GenAI, and alot of people use LLM when they mean GenAI.
This is a really hopeful result for GenAI (fitting deep models tuned by gradient descent on large amounts of data), and IMO this is possible because of specific domain knowledge and approaches that aren't there in the usual LLM approaches.
Do you mean that in this case, it was not a LLM?
It is far more than an LLM, and math != "language".
The second one being backed by a model.
> It is far more than an LLM
It's an LLM with a bunch of tools around it, and a slightly different runtime that ChatGPT. It's "only" that, but people - even here, of all places - keep underestimating just how much power there is in that.
> math != "language".
How so?
LLM = Large Language Model. Large refers to both the number of parameters (and in practice, depth) of the model, and also implicitly the amount of data used for training, and "language" means human (i.e. written, spoken) language. A Vision Transformer is not an LLM because it is trained on images, and AlphaFold is not an LLM because it is trained molecular configurations.
Aristotle works heavily with formalized LEAN statements and expressions. While you can certainly argue this is a language of sorts, it is not at all the same "language" as the "language" in LLMs. Calling Aristotle an "LLM" just because it has a transformer is more misleading than truthful, because every other single aspect of it is far more clever and involved.
There is no reason or framing where you can say Aristotle isn't a language model.
It's an LLM.
It is far more than an LLM, and math != "language".
1. "The search algorithm is a highly parallel Monte Carlo Graph Search (MCGS) using a large transformer as its policy and value functon." ... "We use a generative policy to take progressively widened [7] samples from the large action space of Lean tactics, conditioning on the Lean proof state, proof history, and, if available, an informal proof. We use the same model and prompt (up to a task token) to compute the value function which guides the search."
See that 'large transformer' phrase? That's where the LLM is involved.
2. "A lemma-based informal reasoning system which generates informal proofs of mathematical state-ments, breaks these proofs down into lemmas, formalizes each lemma into Lean, and iterates this process based on formal feedback" ... "First, the actions it generates consist of informal comments in addition to Lean tactics. Second, it uses a hidden chain of thought with a dynamically set thinking budget before predicting an action."
Unless you're proposing that this team solved AGI, "chain of thought" is a specific term of art in LLMs.
3. "A geometry solver which solves plane geometry problems outside of Lean using an approach based on AlphaGeometry [45]." ... following the reference: "AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. "
AlphaGeometry, like all of Deepmind's Alpha tools, is an LLM.
Instead of accusing people of not reading the paper, perhaps you should put some thought into what the things in the paper actually represent.
Please remember that this is a theorem about integers that is subject to a fairly elementary proof that is well-supported by the existing Mathlib infrastructure. It seems that the AI relies on the symbolic proof checker, and the proofs that it is checking don't use very complex definitions in this result. In my experience, proofs like this which are one step removed from existing infra are much much more likely to work.
Again though, this is really insanely cool!!
People are still going to be moving the goalposts on this and claiming it's not all that impressive or that the solution must have been in the training data or something, but at this point that's kind of dubiously close to arguing that Terence Tao doesn't know what he's talking about, which to say the least is a rather perilous position.
At this point, I think I'm making a belated New Years resolution to stop arguing with people who are still staying that LLMs are stochastic parrots that just remix their training data and can never come up with anything novel. I think that discussion is now dead. There are lots of fascinating issues to work out with how we can best apply LLMs to interesting problems (or get them to write good code), but to even start solving those issues you have to at least accept that they are at least somewhat capable of doing novel things.
In 2023 I would have bet hard against us getting to this point ("there's no way chatbots can actually reason their way through novel math!"), but here we are are three years later. I wonder what comes next?
Or are you just saying that solving novel problems involves remixing ideas? Well, that's true for human problem solving too.
What? If we're discussing novel synthesis, and it's being contrasted with answer-from-search / answer-from-remix.. the problem does not matter. Only the answer and the originality of the approach. Connecting two fields that were not previously connected is novel, or applying a new kind of technique to an old problem. Recognizing that an unsolved problem is very much like a solved one is search / remix. So what happened here? Tao says it is
> is largely consistent with other recent demonstrations of AI using existing methods to resolve Erdos problem
Existing. Methods. Tao also says "This is a demonstration of the genuine increase in capability of these tools in recent months". This is the sentence everyone will focus on, so what is that capability?
> the more interesting capability revealed by these events is the ability to rapidly write and rewrite new versions of a text as needed, even if one was not the original author of the argument.
Rejoice! But rejoice for the right reasons, and about what actually happened. Style and voice transformations, interesting new capabilities for fuzzy search. Correct usage of external tools for heavy-lifting with symbolics. And yes, actual problem solving. Novel techniques, creativity, originality though? IDK, sounds kind of optimistic based on the detail here.
>the problem does not matter.
Really? All of the other Erdős problems? Millennium Problems? Anything at all? This gets us directly into the territory of "nothing can convince us otherwise".
Though I suppose if the problem statement in Lean is human-generated and there are no ways to "cheat" in a Lean proof, the proof could be trusted without understanding it
Edit: Found it here https://github.com/plby/lean-proofs/blob/main/src/v4.24.0/Er...
1. This result is very far from showing something like "human mathematicians are no longer needed to advance mathematics".
2. Even if it did show that, as long as we need humans trained in understanding maths, since "professional mathematicians" are mostly educators, they probably aren't going anywhere.
Educator business survived so far, only because they provided in-person interactive knowledge transfer and credentials - both were not possible by static sources of knowledge such as libraries and internet. But now all that is possible without involvement of human teachers.
The METR institute predicts that the length of tasks AI agents can complete doubles every 7 months.
We should expect it to take until 2033 before AI solves Clay Institute-level problems with 50% reliability.
And once we have that formalized corpus, it's all set up as training data for moving forward.
https://github.com/teorth/erdosproblems/wiki/AI-contribution...
It classifies the advancements based on the level of AI input. In particular, the entry in Table 1 related to the original post has both a green and yellow light, reflecting the skepticism from others.
We asked you about this just recently, but it's still most of what you're posting. You're making the site worse by doing this, right at the point where it's most vulnerable these days.
Your comment here is a shallow dismissal of exactly the type the HN guidelines ask users to avoid here:
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something." (https://news.ycombinator.com/newsguidelines.html)
Predictably, it led to by far the worst subthread on this article. That's not cool. I don't want to ban you because you're also occasionally posting good comments that don't fit these negative categories, but we need you to fix this and stop degrading the threads.
If you're interested, https://news.ycombinator.com/item?id=46515507 and https://news.ycombinator.com/item?id=46508115 are other places I wrote about this recently.
It's the biggest problem facing HN, in my opinion.
Surprisingly the latest increase in polarization around generative AI has impacted Hacker News the least our of all tech social spaces.
What I'm interested in is how well HN does at fulfilling its own mandate in its own terms. On that scale, it's getting worse—in this respect, at least, which is a big one. We're going to do something about it, the same way we've always tried to stave off the decline of this place (https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...).
If you're right that the phenomenon is affecting other places even more than HN then I guess we get to be the-worst-internet-forum-except-for-all-the-others even more than before (https://news.ycombinator.com/item?id=13494318, https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...) - not a bad outcome for HN, though I think it might be overly optimistic.
I don't mean to express doubt (you would know better than I) but what makes you say that?
Do you think it's a consequence of external factors like the current political climate or of internal factors like HN getting larger?
I'm not sure it's about HN getting larger, though. It's a bit hard to tell, but at least some of the upswing in cynical and curmudgeonly comments is coming from established users.
It seems to me that in your view the sheer openness to evaluate LLM use, anecdotally or otherwise, is already a bias.
I don't see how that's sensible, given that to evaluate the utility of something, it's necessary to accept the possibility of that utility existing in the first place.
On the other hand, if this is not just me strawmanning you, your rejection of such a possibility is absolutely a bias, and it inhibits exploration.
To willfully conflate finding such an exploration illegitimate with the findings of someone who thinks otherwise as illegitimate, strikes me as extremely deceptive. I don't appreciate being forced to think with someone else's opinion covertly laundered in very much. And no, Tao's comments do not meet this same criteria, as his position is not covert, but explicit.
That's the best part! They don't even need to, because ChatGPT will happily do its own private "literature search" and then not tell you about it - even Terence Tao has freely admitted as much in his previous comments on the topic. So we can at least afford to be a bit less curmudgeonly and cynical about that specific dynamic: we've literally seen it happen.
Also known as model inference. This is not something "private" or secret [*]. AI models are lossily compressed data stores and will always will be. The model doesn't report on such "searches", because they are not actual searches driven by model output, but just the regular operation of the model driven by the inference engine used.
> even Terence Tao has freely admitted as much
Bit of a (willfully?) misleading way of saying they actively looked for it on a best effort basis, isn't it?
[*] A valid point of criticism would be that the training data is kept private for the proprietary models Tao and co. using, so source finding becomes a goose chase with no definitive end to it.
An I think valid counterpoint however is that if locating such literature content is so difficult for subject matter experts, then the model being able to "do so" in itself is a demonstration of value. Even if the model is not able to venture a backreference, by virtue of that not being an actual search.
This is reflected in many other walks of life too. One of my long held ideas regarding UX for example is that features users are not able to find "do not exist".
I now see in the other subthread what they mean.
If that was the case, I apologize for misreading you! If you're interested, I can point you to https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que... for past explanations about this type of misunderstanding and how to avoid it in the future.
For what it's worth, it's not even that I don't see merit to their points. I'm just unable to trust that they're being genuine, not the least for how they conduct themselves (which I only fault them for so much). This also impacts my ability to reason about their points clearly.
Sadly, I'm not able to pitch any systematic solutions.
I get how it's activating and annoying when moderators show up and start fault-finding, so I can appreciate the irritation here. But really, we're just trying to have an internet forum that doesn't destroy itself. I can't imagine why you wouldn't want to contribute positively to that.
Meanwhile I can’t get Claude code to fix its own shit to save my life.
If you think about it, it's also what a lot of other intellectual activity looks like, at least in STEM.
Maybe this should give you some hint to that you're trying to use it in a different way than others?