The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next
The paper discusses the limitations of current leaderboard systems in evaluating frontier models in machine learning. It highlights the need for new metrics that better capture the interactions between model capabilities. The author proposes a playbook for diagnosing and measuring these capabilities over time.
- ▪Leaderboards do not effectively reveal the interactions between model capabilities across releases.
- ▪The study analyzes 34 models from 10 labs and finds that capabilities cooperate, but this cooperation varies by lab and over time.
- ▪The author provides a three-level playbook for measuring and diagnosing model capabilities, along with actionable recommendations.
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Computer Science > Machine Learning arXiv:2605.18840 (cs) [Submitted on 13 May 2026] Title:The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next Authors:Adil Amin View a PDF of the paper titled The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next, by Adil Amin View PDF HTML (experimental) Abstract:Leaderboards rank frontier models on independent axes but do not reveal whether capabilities reinforce or trade off across releases -- and at the frontier, this interaction is the more informative signal.
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