cd /news/artificial-intelligence/ai-discovery-in-combinatorics-from-p… · home › topics › artificial-intelligence › article
[ARTICLE · art-153285] src=arxiv.org ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

AI Discovery in Combinatorics: From Problems to Solutions and Back

A paper submitted to arXiv on 12 October 2026 introduces qtBench, a benchmark of 29 open problems in algebraic combinatorics that ask for simple combinatorial algorithms explaining given polynomial coefficients or identities. The authors built a scaffold for AI agents to search for such algorithms, counter cheating attempts and test candidate solutions, and collaborated with OpenAI, whose researchers obtained proposed solutions to several of the problems. The paper uses those results to raise meta-questions about whether AI discovery at scale is ethical, how results should be released and how proof digestion should be organized.

read2 min views1 publishedOct 19, 2026
AI Discovery in Combinatorics: From Problems to Solutions and Back
Image: source
  [Submitted on 12 Oct 2026]


[View PDF](https://arxiv.org/pdf/2610.16555)

Abstract:We introduce $qt$Bench, a benchmark of 29 open problems in algebraic combinatorics. These problems ask for a simple combinatorial algorithm that explains given polynomial coefficients or identities. To help AI agents search for such algorithms, we provide a scaffold for playing with combinatorial objects, countering cheating attempts, and testing candidate solutions. To explore how far frontier AI systems can go on these problems, we collaborated with OpenAI, whose researchers obtained proposed solutions to several of them. This highlights bigger meta-problems: is AI discovery at scale ethical? How should these results be released? How should proof digestion be organized? We discuss these questions and give our views.

Additional Features

Current browse context:

cs.AI

References & Citations

...

Bibliographic Explorer

(What is the Explorer?) Connected Papers

(What is Connected Papers?) Litmaps

(What is Litmaps?) scite Smart Citations

(What are Smart Citations?) alphaXiv

(What is alphaXiv?) CatalyzeX Code Finder for Papers

(What is CatalyzeX?) DagsHub

(What is DagsHub?) Gotit.pub

(What is GotitPub?) Hugging Face

(What is Huggingface?) ScienceCast

(What is ScienceCast?) Influence Flower

(What are Influence Flowers?) CORE Recommender

(What is CORE?) arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

── more in #artificial-intelligence 4 stories · sorted by recency
── more on @arxiv 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
→ Live at https://your-agent.zahid.host ✓
Get free account → Pricing
from €0/mo · no card required
LIVE [news/ai-discovery-in-comb…] indexed:0 read:2min 2026-10-19 · —