AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems
The paper titled 'AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems' introduces a framework for analyzing AI benchmark ecosystems. It highlights the measurement noise in leaderboard scores and proposes methods to quantify the sources of ranking variance. The authors provide insights into benchmark dynamics and suggest improvements for benchmark design and trustworthiness.
- ▪The study analyzes over 4,000 models from the Open LLM Leaderboard to understand ranking variances.
- ▪Current reporting practices underestimate the relationships between benchmarks and reveal local dependencies among leaderboard items.
- ▪Contributor metadata accounts for approximately 9% of rank-relevant variance, more than architecture or deployment categories.
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Computer Science > Artificial Intelligence arXiv:2605.25272 (cs) [Submitted on 24 May 2026] Title:AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems Authors:Michael Hardy, Anka Reuel, Lijin Zhang, Jodi M. Casabianca, Sang Truong, Yash Dave, Hansol Lee, Benjamin Domingue, Sanmi Koyejo View a PDF of the paper titled AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems, by Michael Hardy and 8 other authors View PDF HTML (experimental) Abstract:While aggregate leaderboard scores drive AI development, they contain substantial measurement noise whose sources and magnitudes remain unquantified, making it unclear when rankings reflect genuine capability differences versus evaluation artifacts.
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