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.
URL https: //www.frontiersin.org/journals/psychology/ articles/10.3389/fpsyg.2020.01357/full
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AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems
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.