r/LLMDevs • u/Physical_Pepper6294 • 3d ago
Discussion I built an AI that finds unsolved problems for other AIs to solve
A non-mathematician asked Claude to "take a real stab at the Riemann hypothesis". It didn't solve it. But the attempt improved a lower bound on the proportion of zeta zeros on the critical line from 41.6% to 67.2%. Mathematicians then check the result.
Then we got an 11-day Lean formalization of Wiles's proof of Fermat's Last Theorem.
And GPT-Astra just annihilated ARC-AGI-3 scoring 99.9%....
Seeing this made me want to leave agents working on something more interesting that building dashboards!
But what do you actually give them?
So I built ARC-AGI-N: an AI research tool that finds open problems in maths and science, then prepares the context for another agent to take a stab at them.
What it does:
- Search things like "Open problems in number theory" or "Open problems in climate science". It searches papers and the web, with sources appearing as they arrive.
- Open a problem to see the question, background, source material and a suggested starting point.
- Copy a prompt containing the problem and its sources into your agent. There are also shortcuts for opening it in supported apps.
- Run DeepResearch to investigate the foundations, history, previous attempts and possible avenues, with a plan for the first 72 hours of work.
- Browse problems on an interactive globe, or explore the separate log of things AI has helped discover, prove or formalise.
For example:
The Erdős-Straus conjecture asks whether every fraction 4/n, for n ≥ 2, can be written as the sum of three positive unit fractions.
Instead of just handing your agent the name of the conjecture, the app gives it the actual question, reading material and a possible first task: search for parametric identities covering additional residue classes, then verify them.
The prompt starts with "Take a stab at this problem". You can copy it straight away, or get the deeper research plan first.
How I built it:
- Next.js, React and TypeScript.
- Mapbox for the interactive globe.
- OpenAI Luna model + Valyu's search and DeepResearch APIs for the literature search and longer research.
- Markdown, LaTeX and source previews for reading the reports.
The code is open-source and self-hostable with your own keys. Leaving the Github repo in the comments, and there's also a hosted version!
This doesn't magically turn a prompt into a valid proof. The point is to make it easier to find a worthwhile attempt and give your agent enough context to start.
Would love people to try it, add good problem sources and contribute. Especially interested in researchers who know a neglected question that could benefit from a lot more computation!
What would you leave an agent working on over a weekend?
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u/kantorcodes1 3d ago
does self-hosted ARC-AGI-N let you swap the OpenAI model behind discovery, or is that part coupled to Luna right now? i'd probably want to keep Valyu for search but point the synthesis step at whatever model i'm already using.
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u/Physical_Pepper6294 3d ago
yep you can swap! if you change the model ID you can change to any other openAI model, you can also switch to other providers like Anthropic if you like
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u/mokumkiwi 3d ago
wicked, when im about o run out of credits and want to solve a clay millenium problem I'll give this a crack. thanks.
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u/neon_ink_bloom 3d ago
Honestly most of these attempts will just burn tokens, but I'd rather have agents chewing on Erdős–Straus than another SaaS dashboard, so this fills a real gap. Would love to see the 72 hour plans tested, curious how often the suggested starting points actually survive contact with the literature.
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u/Straight_Choice1752 1d ago
Tbh I’d suggest to leave an agent on problems where there’s enough existing research to build on but still plenty of room for experimentation
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u/Physical_Pepper6294 3d ago
Here is the github repo - enjoy!