What sort of maths are LLMs good at?
- ID
- 13845
- Status
- summarized
- Published
- 12 Aug 2026, 6:04 PM
- Fetched
- 14 Aug 2026, 5:17 PM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://gowers.wordpress.com/2026/08/12/what-sort-of-maths-are-llms-good-at/
- Source URL
- https://hnrss.org/best
Summary
- Score
- 6.5
- Created
- 14 Aug 2026, 6:21 PM
- Tags
- Audience
- ai_ml_learnersai_agent_usersdevelopers
What happened
Written shortly after OpenAI announced it had solved ten major open problems in mathematics and theoretical computer science—including the first construction of a non-sofic group and a superexponential growth proof for multicolour Ramsey numbers—this post observes that LLMs' most famous mathematical successes have overwhelmingly involved finding counterexamples rather than constructing proofs. The author explores whether this pattern reflects a genuine structural strength of LLMs and what it might reveal about where they still fall short of human mathematicians.
Why it matters
If you build or rely on LLM-based reasoning tools, this suggests a concrete asymmetry: LLMs may be more reliable at disproof-by-counterexample than at constructing novel proofs, which should shape how you scope tasks for agentic math or formal-verification workflows. The author also notes that despite headline results, LLMs are not uniformly better than humans at all mathematics—if they were, their speed advantage would produce a flood of results that has not materialized.
Discussion angle
When wiring LLMs into coding or verification agents, should we explicitly route 'find a counterexample' tasks differently from 'construct a proof' tasks, given the apparent performance gap?