The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
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An embedding-based method learns solver-problem relationships from performance data to predict optimal configurations for linear solvers on unseen problems, achieving 17% better accuracy than feature-based models on SuiteSparse matrices.
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The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
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Embedding-based Methods for Linear Solver Performance Prediction
An embedding-based method learns solver-problem relationships from performance data to predict optimal configurations for linear solvers on unseen problems, achieving 17% better accuracy than feature-based models on SuiteSparse matrices.