recent caltech grad here! and know some of the organizers well
caltech's cs department is very, very weak, and has struggled to recruit top people in the last few years, and the most recent AI faculty hires have had issues. this is very slowly changing but a lot of the motivation for htis was to create a way for students to get ml "recognition" and learn about ai since it cant be done through the school right now. really glad to see hn picked this up!
Won't deny that this is an interesting idea, but I feel like waiting on the output of an LLM for 40 hours feels like it is completely antithetical to what makes classic Hackathons appealing / educative.
More generally, I don't think the shape of a hackathon (intensely working for a short timespan) maps at all onto the way LLM Math progress has seemingly been made so far; AFAIK it mostly involves picking out something for the Model, then having it run for a week with sporadic correction / encouragement.
Have you done any math hacking with sol/astra or fable? It’s more fun than using them for coding. The models are great at the monotony, like constructing a Gröbner-basis, etc. But they’re all still absolutely awful at coming up with new ideas, new proof methods, or new constructive forms. So you spend all your time on coming up with novel hypotheses yourself and handing off the rote work to an agent.
It’s also quite fun to get instant results by finding isomorphisms into unfamiliar areas of mathematics that previously would’ve required some networking in order to build a collaborative relationship.
"A mathematician is a person who can find analogies between theorems; a better mathematician is one who can see analogies between proofs and the best mathematician can notice analogies between theories. One can imagine that the ultimate mathematician is one who can see analogies between analogies."
I wonder how models perform on finding analogies between analogies
But it's not "waiting on the output of an LLM for 40 hours" any more than a regular hackathon is "waiting for my damn teammates to finish their part for 40 hours". From my experience using agentic coding for hackathons, the best teams are those that coordinate with the AI agents in relatively quick cadence, generally giving it small tasks and steering it often. Teams may want to run some long-running sessions too, especially closer to the deadline, but even then, they'd probably want to run and follow several sessions in parallel, and continuously inspect their work so that they have reasonable confidence that their main efforts will wrap up before the deadline. There is an art to it.
If the goal is to accomplish something then why limit yourself with available tools?
I’m not a full on AI optimist but it is absolutely the most powerful tool in a host of applications. From a Hackathon perspective, obviously in the 90s it was much more unorganized, but the same ethos existed. Use all available tools to accomplish the goal/task, it’s where a lot of incredible learning came out of. The same will hopefully happen in scenarios like this one
When Claude made progress on the Riemann conjecture, here are the kind of prompts used:
> Jarred's input was mostly limited to sending Claude messages of encouragement (mostly variants of “keep going” or “believe in yourself”).2 This seems to have helped Claude overcome some initial skepticism that it could make meaningful progress.
And left it for a long time. Jarred isn't a mathematician, he's the maintainer of a janky JavaScript environment.
Prompting for some of the results was almost the "Computer, do a breakthrough. Make no mistakes." meme. Just someone telling the model to keep trying a couple of times.
Unfortunately we don't actually know what kind of prompting was done for the more prominent results.
Same here, it's one thing if I'm just screwing around, but if I'm trying to do anything serious, I need to at least have a handle on what it's doing, and, when thinking traces are available, keeping track of any logical errors in the model's reasoning.
But the goal here is not to run 42km; to stay with the outdoors metaphor, it's more like deciding where and how to set up a bivouac - use whatever tools you have at your disposal to analyze the area you're in, and find the best site to stay in overnight.
Because the companies that run frontier models are malevolent by every metric.
They are destroying the environment, especially those in neighborhoods of low income people.
They are empowering their owners who are some of the most deplorable and duplicitous people living.
They are destroying personal compute to avoid competition with local models by buying all computer components with “promised money” and forcing their P into AI.
They stole the entire creative output of humanity and are trying to sell it back to us.
They are only good for giving wealth access to skill while removing from the skilled the ability to access wealth.
They are being used to kill in war and for surveillance.
Seriously why would you use them? Your use only emboldens them; making you complicit in their nefarious success.
Did you read the article? This is not a hackathon where you build software, it’s one where you’re trying to get a model to make progress on a frontier math problem. The point is that that activity may not map well onto the shape of a hackathon
No it's not, there are tons of programs in mathematics where you go there, form teams, work a problem as a team that's likely to get a result, and then publish the results from all the groups in the conference proceedings. They are called Research Collaboration Workshops.
Very consistent pattern from these tech companies in their mathematics press releases that shows a conspicuous lack of experience in the research math world.
Should I read this as the big labs trying to move maths forward? Or the big labs trying to use professional mathematitians as (cheap?) Labour for validating LLM outputs? On yesterdays "An Alien Mind" post from openAI they openly said that maths is not a priority for them, so I personally know what to think...
Obviously the latter - this fact is betrayed by how the page lists the S^6 complex structure result, which was released as a 100 page barely readable mess (in fact even this might be too charitable), as still "unverified". Clearly a situation labs would like to avoid for future claimed results.
Probably many mathematicians want answers to the questions from the page:
> This AI advancement raises the following questions: (a) How much can AI speed up the process from ideation to peer-reviewed publication? (b) What is the role of a mathematician when AI can solve conjectures faster?
and the big AI companies agreed to sponsor them to find out because it is good publicity for the companies.
Recently I care about how to create harness for mathematics that uses up the complete reasoning ability of the model. I care about both capability and cost.
Most generic harness we have now are not made for maximizing reasoning. I've tested agents like codex, and rarely the cost of reasoning tokens reaches more than 20%. Which is quite strange as math requires a lot of reasoning. So hackathons can be a good test bed.
recent caltech grad here! and know some of the organizers well
caltech's cs department is very, very weak, and has struggled to recruit top people in the last few years, and the most recent AI faculty hires have had issues. this is very slowly changing but a lot of the motivation for htis was to create a way for students to get ml "recognition" and learn about ai since it cant be done through the school right now. really glad to see hn picked this up!
Won't deny that this is an interesting idea, but I feel like waiting on the output of an LLM for 40 hours feels like it is completely antithetical to what makes classic Hackathons appealing / educative.
More generally, I don't think the shape of a hackathon (intensely working for a short timespan) maps at all onto the way LLM Math progress has seemingly been made so far; AFAIK it mostly involves picking out something for the Model, then having it run for a week with sporadic correction / encouragement.
Have you done any math hacking with sol/astra or fable? It’s more fun than using them for coding. The models are great at the monotony, like constructing a Gröbner-basis, etc. But they’re all still absolutely awful at coming up with new ideas, new proof methods, or new constructive forms. So you spend all your time on coming up with novel hypotheses yourself and handing off the rote work to an agent.
It’s also quite fun to get instant results by finding isomorphisms into unfamiliar areas of mathematics that previously would’ve required some networking in order to build a collaborative relationship.
"A mathematician is a person who can find analogies between theorems; a better mathematician is one who can see analogies between proofs and the best mathematician can notice analogies between theories. One can imagine that the ultimate mathematician is one who can see analogies between analogies."
I wonder how models perform on finding analogies between analogies
> I wonder how models perform on finding analogies between analogies
Load-bearingly verbose, in my experience.
Well, I suppose that explains monads. It’s one thing to see an analogy and quite another to make it the basis of an API.
But it's not "waiting on the output of an LLM for 40 hours" any more than a regular hackathon is "waiting for my damn teammates to finish their part for 40 hours". From my experience using agentic coding for hackathons, the best teams are those that coordinate with the AI agents in relatively quick cadence, generally giving it small tasks and steering it often. Teams may want to run some long-running sessions too, especially closer to the deadline, but even then, they'd probably want to run and follow several sessions in parallel, and continuously inspect their work so that they have reasonable confidence that their main efforts will wrap up before the deadline. There is an art to it.
Are you sure it’s letting it run and not going back and forth interactively?
If the goal is to accomplish something then why limit yourself with available tools?
I’m not a full on AI optimist but it is absolutely the most powerful tool in a host of applications. From a Hackathon perspective, obviously in the 90s it was much more unorganized, but the same ethos existed. Use all available tools to accomplish the goal/task, it’s where a lot of incredible learning came out of. The same will hopefully happen in scenarios like this one
It seems like making progress on math is letting the AI run fully autonomously for a few days, occasionally asking it to keep going.
I'm not sure people need to organize a mathathon to wait for a computer to give a printout. They mainly need tokens.
Are they actually autonomous? I’d say subject knowledge at the prompt stage plays a large part towards getting proper results
When Claude made progress on the Riemann conjecture, here are the kind of prompts used:
> Jarred's input was mostly limited to sending Claude messages of encouragement (mostly variants of “keep going” or “believe in yourself”).2 This seems to have helped Claude overcome some initial skepticism that it could make meaningful progress.
And left it for a long time. Jarred isn't a mathematician, he's the maintainer of a janky JavaScript environment.
Here's the transcript: https://www-cdn.anthropic.com/8a0d1add3c637b858a9a181e98c40e...
Prompting for some of the results was almost the "Computer, do a breakthrough. Make no mistakes." meme. Just someone telling the model to keep trying a couple of times.
Unfortunately we don't actually know what kind of prompting was done for the more prominent results.
It’s definitely not how I work, I’d need to read the model responses and set a direction for the model to go
Same here, it's one thing if I'm just screwing around, but if I'm trying to do anything serious, I need to at least have a handle on what it's doing, and, when thinking traces are available, keeping track of any logical errors in the model's reasoning.
I think the purpose of an event like this would be to optimize the process so that it isn't just occasionally asking an AI to keep going.
If the goal is to run 42km why limit yourself? Use a car and win.
But the goal here is not to run 42km; to stay with the outdoors metaphor, it's more like deciding where and how to set up a bivouac - use whatever tools you have at your disposal to analyze the area you're in, and find the best site to stay in overnight.
> why limit yourself with available tools
Because the companies that run frontier models are malevolent by every metric.
They are destroying the environment, especially those in neighborhoods of low income people.
They are empowering their owners who are some of the most deplorable and duplicitous people living.
They are destroying personal compute to avoid competition with local models by buying all computer components with “promised money” and forcing their P into AI.
They stole the entire creative output of humanity and are trying to sell it back to us.
They are only good for giving wealth access to skill while removing from the skilled the ability to access wealth.
They are being used to kill in war and for surveillance.
Seriously why would you use them? Your use only emboldens them; making you complicit in their nefarious success.
I for one, am one who walks away from Omelas.
Did you read the article? This is not a hackathon where you build software, it’s one where you’re trying to get a model to make progress on a frontier math problem. The point is that that activity may not map well onto the shape of a hackathon
No it's not, there are tons of programs in mathematics where you go there, form teams, work a problem as a team that's likely to get a result, and then publish the results from all the groups in the conference proceedings. They are called Research Collaboration Workshops.
Very consistent pattern from these tech companies in their mathematics press releases that shows a conspicuous lack of experience in the research math world.
Should I read this as the big labs trying to move maths forward? Or the big labs trying to use professional mathematitians as (cheap?) Labour for validating LLM outputs? On yesterdays "An Alien Mind" post from openAI they openly said that maths is not a priority for them, so I personally know what to think...
Obviously the latter - this fact is betrayed by how the page lists the S^6 complex structure result, which was released as a 100 page barely readable mess (in fact even this might be too charitable), as still "unverified". Clearly a situation labs would like to avoid for future claimed results.
Probably many mathematicians want answers to the questions from the page:
> This AI advancement raises the following questions: (a) How much can AI speed up the process from ideation to peer-reviewed publication? (b) What is the role of a mathematician when AI can solve conjectures faster?
and the big AI companies agreed to sponsor them to find out because it is good publicity for the companies.
Yeah it's a bit of a tricky situation. It's almost like a bribe in a sense.
I've applied as a team, hope I get in.
Recently I care about how to create harness for mathematics that uses up the complete reasoning ability of the model. I care about both capability and cost.
Most generic harness we have now are not made for maximizing reasoning. I've tested agents like codex, and rarely the cost of reasoning tokens reaches more than 20%. Which is quite strange as math requires a lot of reasoning. So hackathons can be a good test bed.
> It will be the first hackathon ever devoted to research level mathematics.
Well, that's pretty damned ignorant; I was attending William Stein's hackathons on the BSD conjecture and the Sage Math project nearly 2 decades ago.
Interesting, if only I still have energy to work on something 40 hours non-stop
Will BigAI support this with free access to lots of hardware loaded with frontier models?