AlphaProof
Google DeepMind's reinforcement-learning system that writes formally verified math proofs in Lean.
Overview
AlphaProof is a Google DeepMind reinforcement-learning system that proves mathematical statements within the Lean 4 formal theorem prover, so every step is machine-checked. Paired with AlphaGeometry 2, it reached silver-medal-standard performance at the 2024 International Mathematical Olympiad, solving several of the hardest problems including the number-theory Question 6. The research was published in Nature. AlphaProof is a research system, not a public product — there is no app or self-serve access.
ASEAN Perspective
AlphaProof in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
What this is for: Automatically finding and formally verifying rigorous mathematical proofs inside the Lean theorem prover.
Who this is for: Mathematics and AI researchers interested in automated reasoning and formal verification.
Availability: A DeepMind research system with published results in Nature — not a public product; there is no app or self-serve access.
What people say
AlphaProof is a DeepMind research system, not a consumer product, so its "reviews" are its scientific reception, which has been strong. Paired with AlphaGeometry 2, it reached silver-medal standard at the 2024 IMO (28 points, solving several of the hardest problems including Question 6), and its scoring was overseen by Fields Medallist Tim Gowers and IMO gold medallist Joseph Myers. Terence Tao and others publicly praised it for expanding the boundaries of AI in mathematics. The method was published in Nature in November 2025.
Mathematicians also voiced substantive criticism. Kevin Buzzard (Imperial) pointedly asked whether these systems have told us anything new, and concluded they haven't. The solutions apply known techniques to neglected problems, not mathematical creativity. Others flagged the enormous compute cost (test-time reinforcement learning per problem plus tens of thousands of TPU-days to train), likening it to "national-laboratory-scale" apparatus that ordinary researchers cannot reproduce.
There was also transparency criticism: DeepMind announced the result more than a year before publishing peer-reviewed methods, and the wider math community has since debated heavy AI use, including a mathematicians' declaration urging restraint. Impressive and rigorous, but costly, closed for a long stretch, and not yet creative.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including AlphaProof's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with AlphaProof unless explicitly stated.
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