Google Deepmind's AlphaProof Nexus solves decades-old math problems for a few hundred dollars
Google Deepmind's AlphaProof Nexus has autonomously solved nine open Erdős problems, including two that stumped mathematicians for 56 years, for just a few hundred dollars per problem in inference costs. Unlike OpenAI's natural-language approach, the system uses the Lean compiler to verify every proof step automatically. Still, the overall success rate sits at just 2.5 percent.
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Key points
- AlphaProof Nexus autonomously solved nine open Erdős problems, including two that had remained unsolved for 56 years.
- Each problem cost only a few hundred dollars in inference costs.
- It uses the Lean compiler for automatic proof verification, unlike OpenAI's natural-language method.
- The overall success rate is only 2.5 percent.
Why it matters
This matters because alphaProof Nexus autonomously solved nine open Erdős problems, including two that had remained unsolved for 56 years.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
Google Deepmind's AlphaProof Nexus has autonomously solved nine open Erdős problems, including two that stumped mathematicians for 56 years, for just a few hundred dollars per problem in inference costs. Unlike OpenAI's natural-language approach, the system uses the Lean compiler to verify every proof step automatically. Still, the overall success rate sits at just 2.5 percent.
The article Google Deepmind's AlphaProof Nexus solves decades-old math problems for a few hundred dollars appeared first on The Decoder.