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GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

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Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#451
post #220
post #217

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To be able to propose interesting conjectures you need to walk the walk of trying to prove things yourself. That's not great for future generations.

It is the same with anything technical; you need taste and wisdom, which are borne of experience. I think society will want to subsidize this learning lest it deskills en masse.

But we can't have that experience when we deem the experience acquisition as sub-optimal to the grand scheme of things, which is to produce software.

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#452

If all checks out this is a huge milestone. AI has now solved one of the most famous open problems in graph theory, using an off the shelf model, in one hour. It might be a better mathematician than most humans at this point. Kind of like when chess software started beating everyone except grandmasters. What’s left? Proposing and building out entirely new theories and frameworks? Then better than any human? Then alie…

You don't compete with a chainsaw at cutting trees. You decide what the tree is for. Then sell the rainforest for shareholder value. https://youtu.be/UrgpZ0fUixs

And now AIs can do all that

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#453
post #196

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> a human mathematician would aspire to Some do. But there's also the notion that a clever trick is a bad explanation.

But a proof isn’t an explanation it’s a proof. Proof by assuming the opposite is true and demonstrating a contradiction is very indirect and not at all directly explanatory yet it’s a proof non the less. The goal of proofs is to demonstrate something to be provably true, not expository knowledge gathering. In fact most mathematicians (myself included!) think the more clever the trick the better the proof! The trick i…

Thank you for the validation. I only dabble in mathematics - so it's reassuring to hear what I remember - a clever proof is still considered a positive thing.

I do (of course) understand the issue that non-constructive proofs can be frustrating - and a constructive proof of the same theorem is valuable tool in seeing exactly why a truth is true.

But I guess I've never seen a non-constructive proof (in a space where no constructive proof is available) considered a failure.

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#454
post #269
post #171

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> Once on a late-night session, I had Cline!Claude spontaneously point out the time to me and suggest that I get to bed and come back fresh the next day. I had Claude say something "It's getting late, let's pick this up tomorrow" at like 11am. As for context, in my experience Claude starts trying either to do maximum work with minimum tokens when it's approaching limit, or it starts deferring useful work while doing…

It's in the training data! Long conversations between humans result in humans getting tired and going to bed. I have this reality baked into my workflow: 1. Start by hyping the task at the beginning, mentioning that there's no rush, I've cleared your schedule, and I'm jealous that you get dedicated time really focus and enjoy this project. 2. Periodically say "Great work, let's finish this next week. Have a great wee…

I was - pleasantly - surprised (and also a bit suspicious) a couple of days ago as I was having Claude resolve a problem in a running service that it had accidentally caused, and it eventually gave up since it involved hitting a 3rd-party API unconventionally. Then I told it that I needed the service working again as it was blocking another task, and immediately in that turn it resolved it.

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#455
post #444

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> a Chinese model it would not have gotten the same attention Well, you would be wrong.

Interesting point I guess, much to consider

Consider actually searching for GLM or other top Chinese models and see how many upvotes they have been getting.

Your claim that Chinese models don’t get as much attention is easily proven false.

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#456

> GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture Very misleading article title. Title should be "Un-named humans produce unverified proof of CDC Conjecture using GPT-5.6" ... but I expect only advertising copy when it comes from the AI industry.

It's been amusing to watch the points bounce up and down on my comment.

I guess equally half the readers agree with my sentiment, and half down-vote, being upset by my attitude to the AI industry :)

PS. I'm quietly waiting for the bubble to pop - the main interest being will it pop with a bang and cause grief to many, or will it just go with a long drawn-out fart that can be ignored by most.

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#457

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That is an incredibly insulting comment. I am married, two children, have lead a fantastic fulfilling life. Just because I don't believe the Flying Spaghetti Monster created the universe doesn't mean I am an "Edgelord". Remember the phrase, you also don't believe in God. There are hundreds of gods you don't believe in.

Bringing up the Flying Spaghetti Monster does make you an edgelord though.

Which deities don't you believe in? I don't believe in all of them. Presumably you don't believe in almost all of them?

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#458

It seems like a solid set of criteria for how easily a task can be automated by AI agents is: - extent to which correctness of solution be easily specified and checked - extent to which new potential solutions can be implemented as text - extent to which prior art exists online This basically maps to software engineering and math. I think a fair bit of AI hype comes from the fact that the very architects of AI are th…

These are true and it does make theses fields the first to fall, but also the hype comes form the fact that it can escape these conditions as well: - generalize to non verifiable domains (https://arxiv.org/abs/2507.17746)[This is on going work but has had steady progress in many angles of attack) - Visual Reasoning is alive and well in video diffusion and image models(see recent works around using diffusion model priors as world model features for physics reasoning) - Prior art exists for anything humans can do online?(Is this one even a hold back?)

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#459
post #76

Earlier quoted context omitted.

Many harnesses include a current date and time in their system prompt, and if there is a way for the model to call for an updated time (either a dedicated time tool or calling the OS' `date` tool) they can track time they spent doing something. If not told up-front, they can try to infer it from timestamps in their logs. Sort of like a human - if you ask them to time something and give them a stopwatch, they do it. I…

I found that telling Claude I was going to bed meant it continued on making assumptions for longer rather than asking lots of questions or stopping part way.

Same - at the end of the day I'd leave my last turn running with something like "I am going to bed so keep going until you are done" and be surprised that in the morning it'd kept going.

Re: GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

#460

Earlier quoted context omitted.

>> What’s left? For example, there's all the problems that the same off-the-shelf model hasn't solved despite OpenAI running it for many hours on them. Don't forget you're only seeing the results of successful runs. We can estimate that those unsolved problems must number in the dozens, or even hundreds, given the amount of time that passed since the last announcement of a solution to an interesting problem by an Ope…

The 2 most notable/interesting solutions have come from Open AI directly, but most of the 'LLM solves open problem' category didn't and has come from 3rd parties doing their own thing with publicly available models. I don't see why one would assume they're running models on hundreds of problems. Most likely they have a few problems they especially care about that they run on.

What, Erdős problems? It's hard to see how anyone except mathematicians, and then again only a few communities of mathematicians, especially care about those.

Remember back in the day when Deep Mind made AlphaGo? That made huge waves for two reasons: one, it was very well understood by AI researchers that beating expert humans at Go was very hard; and, two, that Go is a game of great cultural significance to literally billions of people outside of academia, albeit mainly in SE Asia.

Now, Erdős? I'm a computer scientist and I had to look up the planar unit distance problem when I heard about it because I had no idea what that was. Because it never comes up in the literature I read. I'm not saying it's not interesting, but it does seem a bit... random? That they started with Erdős problems? I'd have gone for a Millennium Prize problem, first. P vs NP, Riemann, Navier Stokes, those are heavy-weight results that would establish AI as the de facto approach to mathematics for the foreseeable future. Even I would find it hard to raise an objection (imagine that). Erdős can take a number, compared to all that.

I'm not saying they're choosing problems at random, mind. But it does seem like we're only seeing the tip of the iceberg, with respect to what the AI companies are doing internally. That shouldn't be a surprise. That's exactly how research works in general, both in academia and in industry. There is a clear survivorship bias and we only ever get to see the positive results, never the negative ones.

>> The 2 most notable/interesting solutions have come from Open AI directly, but most of the 'LLM solves open problem' category didn't and has come from 3rd parties doing their own thing with publicly available models. I don't see why one would assume they're running models on hundreds of problems.

Actually, that's a good point but it's in support of my contention. If there are random people in the community running LLMs on their own, favourite, maths problems, we should be seeing many more of those solved and much more often, provided LLMs were really as good at maths as OpenAI et al want them to be. There must be literally thousands of mathematicians trying to use LLMs to solve this or that problem that is famous in their community. Where are all those spectacular results?

Or, to abuse Fermi's question, where is everybody?

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