In the first 24 hours of GPT-6’s release, I solved one of @EpochAIResearch's FrontierMath Open Problems — whether a stretched Littlewood-Richardson polynomial can have a negative coefficient within the stated size bounds.
Here's how I did it
arxiv:2609.14357
In addition the workflow would prioritize proving one large theorem instead of hundreds of smaller ones, which is a common trap these models fall into.
The prompt was used with goal mode and formally explained what a proof of the problem would entail(similar to the prompt for the CDC).
Over 50 years ago, John Leech posed an open problem in combinatorics.
At 17 years old, using GPT-5.6 sol, I proved that no LeechTrees of order-18 exist.
I found 150+ incremental theorems reducing the search space from 10^57 to 10^10.
1/n
The computer-assisted proof can be found in this repo: https://t.co/z6FkHc3SHj
The computer-assisted proof is complete, but it is not yet end-to-end formalized in Lean. Completing that formalization is part of the future work.
5/n
Congratulations @MaseehG_ for being the first Kelly Grant recipient!
Maseeh took a question Chan and Pak left open about linear extension ratios of posets and proved their bound works in the fixed-gap range.
He worked alone with GPT-5.5.
The first Kelly Grant is already out: @MaseehG_ resolved the fixed-gap case of an open question of Chan & Pak in combinatorics — working with GPT-5.5, proof machine-checked in Lean.
Published on arXiv: https://t.co/Nl8O434Bmu
9 spots remain.
Announcing Kelly Grants: I'm gifting up to $100,000 in AI usage credits & compute to individual researchers.
Up to 10 people, up to $10k each.
No institution required. No equity, no strings attached, just do great research and share it with the world.
Details + how to apply:
Excited to continue working with new and better models on the frontier.
Thank you to Professor Chan for the important guidance - https://t.co/eNFc91EzLe
3/3
In 2024 Professors Chan and Pak at UCLA and Rutgers proposed a case in enumerative combinatorics.
At 16 years old, I used a custom GPT-5.5 harness to disprove this case.
arxiv:2607.10084
1/3
I used a custom harness built around GPT-5.5.
A supervisor model directed several solver agents, every 90 minutes, they reported discoveries, failed approaches and remaining gaps.
The supervisor then updated their instructions and redirected the search.
2/3