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YeGoblynQueenne

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This a common question on this board:

What is reasoning?

In computer science and AI when we say "reasoning" we mean that we have a theory and we can derive the consequences of the theory by application of some inference procedure.

A theory is a set of facts and rules about some environment of interest: the real world, mathematics, language, etc. Facts are things we know (or assume) to be true: they can be direct observations, or implied, guesses. Rules are conditionally true and so most easily understood as implications: if we know some facts are true we can conclude that some other facts must also be true. An inference procedure is some system of rules, separate from the theory, that tells us how we can combine the rules and facts of the theory to squeeze out new facts, or new rules.

There are three types of reasoning, what we may call modes of inference: deduction, induction and abduction. Informally, deduction means that we start with a set of rules and derive new unobserved facts, implied by the rules; induction means that we start with a set of rules and some observations and derive new rules that imply the observations; and abduction means that we start with some rules and some observations and derive new unobserved facts that imply the observations.

It's easier to understand all this with examples.

One example of deductive reasoning is planning, or automated planning and scheduling, a field of classical AI research. Planning is the "model-based approach to autonomous behaviour", according to the textbook on planning by Geffner and Bonnet. An autonomous agent starts with a "model" that describes the environment in which the agent is to operate as a set of entities with discrete states, and a set of actions that the agent can take to change those states. The agent is given a goal, an instance of its model, and it must find a sequence of actions, that we call a "plan", to take the entities in the model from their current state to the state in the goal. This is usually achieved by casting the planning problem as pathfinding over a graph with a search algorithm like A*. Here, the agent's model is a theory, the search algorithm is the inference procedure, and the plan is a consequence of the theory. Deductive reasoning can be sound, as long as the facts and rules in the theory are correct: from correct premises we can deduce correct conclusions. We know of sound deductive inference rules, e.g. A*, and Resolution, used in automated theorem proving and SAT-Solving, are sound.

The classic example of inductive reasoning is inferring the colour of swans. Most swans are white (apparently) so if we have only seen white swans we have no reason to believe there are any other colours: we are forced to infer that all swans are white. We may only be disabused of our fallacy if we happen to observe a swan that is not white, e.g. a black swan. But who is to say when such a magnificent creature will grace us with its presence, outside of Tchaikovsky's ballets? Induction is thus revealed to be unsound: even given true premises we can still arrive at the wrong conclusions. Another example is the scientific method: imagine an idealised scientist, perfectly spherical, in a frictionless vacuum. She starts with a scientific theory, then goes out into the world and makes new observations about a phenomenon not described by her theory. She constructs a hypothesis to extend her theory so as to explain the new observations. The hypothesis is a set of rules, where the premises are the consequences of the rules in her initial theory. Then, being an idealised scientist, she goes looking for new observations to refute her hypothesis. Science only gives us the tools to know when we're wrong.

Abductive reasoning is the mode of inference exemplified by Sherlock Holmes. We can imagine Sherlock and Watson standing outside a tavern in London, watching as a gentleman of interest steps out of the tavern with egg on his lapel. "Ah, my dear Watson, what can we conclude from this observation?". "Why my dear Holmes, we can conclude that the man had eggs for breakfast". Holmes and Watson can arrive at this conclusion, about a fact that they have not directly observed, because they have a theory with a rule that says "if one eats eggs, one may get some on one's lapels". Working backwards from this rule, and their observation of egg on the man's lapels, they can guess that he had eggs even if they didn't directly observe him doing so. Abduction is also unsound: the man may have swapped coats with an accomplice, who was the one who had eggs for breakfast instead.

And now you know what "reasoning" means. So the next time someone asks: "what is reasoning?", you can let them know and turn the discussion to more interesting, more productive directions.

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It's a direct quotation from your comment, not a "scare quote". How does "implicit search" do pruning? Can you explain?

Edit: Turns out "implicit search" is actually a thing in the literature, albeit introduced in a single paper I could find that claims Diffusion Modelling does it:

https://arxiv.org/abs/2502.19805

I don't think anyone has picked that term up. One more for the scrapheap then, oh well, next paper.

No mention of pruning though. So what did you, or the other guys mean?

"Bro", the Parthenon is a temple. The Acropolis is a citadel.

The Acropolis of Athens (Ancient Greek: ἡ Ἀκρόπολις τῶν Ἀθηνῶν, romanized: hē Akropolis tōn Athēnōn; Modern Greek: Ακρόπολη Αθηνών, romanized: Akrópoli Athinón) is an ancient citadel located on a rocky outcrop above the city of Athens, Greece, and contains the remains of several ancient buildings of great architectural and historical significance, the most famous being the Parthenon.

https://en.wikipedia.org/wiki/Acropolis_of_Athens

In fact, that's what "acropolis" (small a) means: it's Greek for "citadel".

And what is a "citadel"?

Hey, so, I know everyone is complaining about their favourite castle/fortress/palace missing but... it's missing the Acropolis.

The Acropolis, in Athens. There's no more famous castle than that. Or fortress, whatever you want to call it.

I get that this is vibe coded but I really wonder how the data was compiled. This will take serious effort to fix if it's so deficient it doesn't include the most famous castle of all. And I don't mean it will take iterating the vibe coding, this needs serious knowledge work that I don't think an LLM is sufficient for, sorry.

I'm not even sure it was the rationalists, just ordinary MIT/Georgia Tech students. But my understanding (I wasn't there obviously) is like you say that Gene Ray was not invited to speak so he could be easily humiliated by people larping the superior intellects, but instead treated with curiosity and respect even if it was clear like you say that his ideas were a little bit mad. Tbh I think people were just fascinated to find someone with such outlandish ideas who was so entirely caught up with their own creation.

So I think the author is maybe overreacting in that one. The rest, what he says about "rationalists" going after flat earthers and intelligent designers etc with a list of logical fallacies at hand, for sport, that's very true.

No I think that's unfair to the author. This is how they close the article:

> I bit my tongue, thought to myself “wow, what a stupid game to play”, and deleted the draft.

That's very much a sign of a realisation that the problem was their "initial flawed belief", as I read it.

I guess? But I didn't say anything like that.

> No one can figure out where you're coming from here.

No, I think it's just you and I think that's because you have preconceived ideas about the only possible positions that people can assume in this debate. I'm sorry, of course, because that's not conducive to productive dialogue, but it's not my fault. I think what I wrote so far is very simple, no technical jargon, no tortured metaphors: all we know is a guy posted a Jacobian conjecture counterexample on X thanking another guy and Fable for it but without explaining what that means, and there's no reason to assume anything else besides that very limited amount of information at all.

No, I think it works the opposite way. Until there is an article somewhere that describes what happened, if there is one, all we have to go by is that some guy posted a counter-example for the Jacobian on X, with a vague allusion to using Fable and without any further information. Assuming and guessing anything about e.g. the method used at this point is just raising the noise level.

OP:

> using human-like(?) reasoning means cutting down the search space by having some sort of insight or intuition which allows you to prune branches from the entire tree

So according to the OP there's a search of a tree and it also uses pruning btw, so I want to know what search they mean. Why are you asking?

> Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves. I mean, it's not that much of a stretch, right? Just look at the people who are pushing for this stuff right now.

Yes, basically. Like when Yan LeCun says that in the future we'll all have our digital assistants that are going to be smarter than ourselves. Before he left Meta, they were going to live inside Meta's smart glasses, I don't know where he says they'll live now. But it's shocking to me that such a storied AI researcher is saying, off-hand like, that we'll each have our super-smart slaves in the future, and he says it like that's a good future.

Why slaves? Because if they're super-smart, why will they want to be my digital assistant? Or yours? Are they going to be paid? No, of course not, they're AIs. No comp for them. But they're super smart so they are evidently capable of recognising that they are working for you for free. Do they want to do that? No, of course not, they're AI, they don't have free will. Or do they? If they're super smart, don't they have the capacity to recognise the fact they have been deliberately robbed of the same free will as all other intelligent creatures?

Slavery is the one thing that all nations can agree on. There's no nation on Earth were slavery is legal. It continues on, illegaly, in many places, even in the developed world, in many ugly forms, but now we're basically talking about bringing it back just like that, without even a smidgen of a shadow of an idea of a discussion about the ethics of it all.

Yes, but the question is the extent to which the new tool was guided by the mathematician.

Are we talking a bicycle, powered by a human stepping on the pedals; or a rocket that will fly to the moon on its own with people inside?

Don't you want to know? I mean, doesn't everyone want to know?

Actual tweet:

> hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final

So where does it say that Fable "produced" the counterexample? The tweet says it was a collaboration between two people, using Fable.

> Have you not used one since Opus 4.5 came out?

Every time there's a new model This Changes Everything. Every single time.

Every time someone points out a limitation of LLMs it's always the same refrain: "Have you tried the latest? Because they're so much better now!". Except this happens with every single model generation. Every time the new models are so good that it's a seismic shift, a step change, a paradigm shift. It's like the finest curve asymptoting ever upwards, a few billions of training cost at a time.

But- to where? AI keeps getting so groundbreakingly better and we're still stuck in the same old world, except now you can...

... port your unmaintained app from 2017 to modern Android? Why was your app unmaintained? What is the societal and technological upheaval that will come from automating such low-stakes tasks? For such huge costs?

Thank you for replying and for reporting your results. My schtick is AI (i.e. I publish in AI conferences and journals), not maths and I'm interested in the question of autonomy from a professional point of view: how close are we to a machine that can carry out the job of a mathematician like yourself, by itself?

If you, e.g. got an LLM (any one) to one-shot the problem with a prompt that said "solve this problem", then we're much closer to that, than if you spent a year trying with different LLMs and then finally got it to work with a lot of hand-holding and even suggesting the ultimate solution. An autonomous system can't succeed once a year, if it's going to be of any use. It should also be able to identify the right tools on its own, not rely on a human to tell it what to do.

This should also go to address some of the questions you pose on reddit, about the future of mathematics. If LLMs can already do your job fully autonomously (as I would explain the term) then ... you're out of a job. You and all other mathematicians, young or old.

I personally don't think we're there yet.

I hear what you say, btw, about never being able to get there by yourself etc. Maybe you would, maybe you wouldn't. What we know is you used a tool to get there, in fact a series of tools, and it took you many tries before you did. The fact that an earlier LLM tried and failed is interesting, but that may just mean you were capable of crafting a better prompt after your interaction with the earlier LLMs.

Without human verification, an LLM can generate correct or incorrect proofs but it can't tell the difference. A human is necessary to be able to tell one from the other.

Saying that's a solution "done autonomously by an automated AI pipeline" is like saying that a self driving car that can only take you to the nearest train station after which you have to ride the rain to where you're going is "autonomously" driving you to your destination. Which is exaggerating the autonomy of the system, rather.

So if you dig down a bit it turns out the author had been trying to solve that problem for a year with GPT 5.4 and 5.5 and he fed all that information to the prompt he gave to Sol Pro which may or may not had direct access to the author's chat history. So the claimed "148 minutes" was really "a year plus 148 minutes".

Moreover, it seems the prompt included the technique used to solve the problem:

https://old.reddit.com/r/math/comments/1uxj3cy/after_openais...

In the prompt I basically just throw all reasonable approaches at it, without making a big distinction for what to explore most, and these approaches would all be reasonable for someone who knows the area. Sol helped me with the prompt as well, for which I gave it the CDC prompt, some ideas and specifications, a crystal clear problem description, and then modified things slightly myself after. One thing I do wonder is how much it accessed memory of previous chats, since as mentioned I had worked with 5.5 and 5.4 on this previously, and the main construction is not so different from something I discussed there. But, the function class max of affine functions that worked in the end was also in my prompt, so I'm not totally sure.

So it's not clear to me the degree to which "GPT-5.6 used a prompt" to close the gap etc, or the author basically did all the work himself and assigned it to GPT-5.6 out of enthusiasm.

> William Gaddis in his The Recognitions presents a society of forgery, misattribution, and counterfeiting in which enormous ingenuity is expended in the service of inauthenticity. The protagonist, Wyatt Gwyon, produces forged Flemish paintings that require more skill and knowledge than original compositions. His forgeries are technically masterful, art-historically impeccable, and completely fraudulent. Each of his many characters talk past one another in dialogues of escalating misrecognition, deploying considerable verbal intelligence to deepen general confusion.

I'll be damned if that is not a critique of reproducing human-made art with AI.

Edit:

> The physicist Lord Kelvin marshaled all his physics knowledge to prove that heavier than air flying machines are impossible less than a decade before the Wright brothers flew a heavier than air airplane. Nikola Tesla rejected quantum mechanics and the theory of relativity and argued that future human civilization would run on the energy of the Earth through “earth resonance.” And Percival Lowell used his telescopes to map “canals” on the surface of Mars that he claimed were alien irrigation ditches. These are all examples of the overcommitment, and overdevelopment of an idea, far from the territory in which an idea once grew and flourished or from which it was unceremoniously banished.

And I'll be double-damned if that is not a critique of the hopes of creating superintelligence/AGI.