AI peer review gets a serious scale test at ICML
Google's PAT found 89.7 percent of tested math proof errors at ICML and STOC, moving AI peer review into real workflow while humans keep the final call.
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Google's PAT found 89.7 percent of tested math proof errors at ICML and STOC, moving AI peer review into real workflow while humans keep the final call.
The unit distance proof gives OpenAI an AI math win: n^1.014 unit pairs, with humans still doing the verification work.
Capability is improving about 15.5 ECI a year, and the rate rose after early 2024. The expected AI plateau never arrived.
The gap between the best AI models and the rest is collapsing, and aggregate benchmark leaderboard scores now hide more than they reveal.
Training compute for frontier models has grown about 5 times a year since 2020. The scatter looks clean, but every data point sits to the left of the real question.
Frontier training costs climb about 3.5 times a year while algorithms get 3 times more efficient. The two trends are racing, and the gap decides who can still compete.