AI has solved 10 long-standing open problems in mathematics — all verified with machine-checked Lean 4 proofs.
Title: Ten advances in mathematics and theoretical computer science
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❓ What problems were solved?
💡 Ten results spanning eight fields: high-dimensional sphere packing, existence of non-sofic groups, disproving Connes's rigidity conjecture, exponential parallel repetition for quantum games, polynomial-factor hardness for the closest vector problem (with post-quantum cryptography implications), Ehrhart's volume conjecture, multicolor Ramsey numbers, and extremal number conjectures (Erdős problems 146, 180, 183). Each problem had seen no progress on its main result for at least a decade.
❓ Which AI solved them?
💡 An internal evaluation version of Astra, OpenAI's next major unreleased model. Astra found the core proof structures; humans then prepared manuscripts and formalized the arguments using the same model. The total compute cost: roughly $2,000 at standard API rates — "theorem proving as a routine batch job any lab can run."
❓ Are these proofs actually correct?
💡 Every result ships with a machine-checkable Lean 4 certificate (mathlib + Lake, Apache-2.0, on GitHub). Running `lake exe cache get && lake build All` either compiles or it doesn't — correctness is resolved in minutes, not months of peer review. This is the strongest verification bar AI-produced mathematics has cleared at this scale.
❓ What does this mean for the future of math research?
💡 The significance is breadth, not a single lucky strike: eight fields, ten results. The bottleneck is no longer compute budget — it shifts to prompt design and result vetting. The rate-limiting step in mathematical discovery may soon be a human's ability to pose the right question and formalize the answer, not AI's ability to find it.
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