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ISSN 0003-1305 · The American Statistician · DOI 10.2307/2683631

1 Pith paper cite this work, alongside 99 external citations. Polarity classification is still indexing.

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cs.AI 1

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2026 1

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UNVERDICTED 1

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AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems

cs.AI · 2026-05-24 · unverdicted · novelty 6.0

CFA and Generalizability Theory applied to LLM leaderboards show latent general-factor slopes are stable (R_g=0.97) while manifest scaling-law slopes are unreliable (R_β=0.53), with contributor metadata explaining more rank variance than architecture.

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  • AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems cs.AI · 2026-05-24 · unverdicted · none · ref 2

    CFA and Generalizability Theory applied to LLM leaderboards show latent general-factor slopes are stable (R_g=0.97) while manifest scaling-law slopes are unreliable (R_β=0.53), with contributor metadata explaining more rank variance than architecture.