Global Bradley-Terry rankings of LLMs are misleading due to structured heterogeneity in user preferences, and small (λ, ν)-portfolios recover coherent subpopulations that cover over 96% of votes with just five rankings.
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A condensed time-expanded network with O(n²μ) nodes and O(μmn) edges solves max flow over time with μ capacity changes in O(μ²n³m) time.
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Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
Global Bradley-Terry rankings of LLMs are misleading due to structured heterogeneity in user preferences, and small (λ, ν)-portfolios recover coherent subpopulations that cover over 96% of votes with just five rankings.
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Brief announcement: A special case of maximum flow over time with network changes
A condensed time-expanded network with O(n²μ) nodes and O(μmn) edges solves max flow over time with μ capacity changes in O(μ²n³m) time.