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Tao, Gómez-Serrano, Georgiev and Wagner test AlphaEvolve on 67 maths problems

★★★scienceGoogle DeepMindUCLABrown Universityconfidence: high

In 'Mathematical exploration and discovery at scale' (arXiv 2511.02864), Bogdan Georgiev, Javier Gómez-Serrano, Terence Tao and Adam Zsolt Wagner ran AlphaEvolve on 67 problems in analysis, combinatorics, geometry and number theory. It rediscovered the best known constructions in most cases and improved several. Some runs were chained with Deep Think and AlphaProof to produce proofs.

Key facts

Science result

Field
mathematics / analysis / combinatorics / geometry
Problem
Broad battery of optimisation-type open problems (e.g. inequalities, packings, finite-field Kakeya-type constructions)
Result
Systematic evidence that LLM-driven evolutionary search matches or beats best-known constructions across dozens of problems.
AI system
AlphaEvolve, Gemini Deep Think, AlphaProof
Human role
Human-led with AI tools: mathematicians chose problems and scorers
Verification
Constructions verifiable; preprint
Status
confirmed

What happened

Leading mathematicians stress-tested AlphaEvolve on a large, varied problem set and published both the successes and the failures.

Why it matters

Coming from Tao, it gave the maths community a credible, balanced picture of what AI search could do, just before the 2026 surge.

Changelog

  • 2026-09-29: created

Related events

  1. AlphaEvolve: Gemini-powered agent discovers new algorithms ★★★★
  2. Adam Zsolt Wagner uses reinforcement learning to find counterexamples to open graph-theory conjectures ★★

Sources (3)

id: 2025-11-05-alphaevolve-tao-67-problems · updated 2026-09-29 · open in the interactive timeline