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MECHVAR: Variance-Guided Mechanism Discrimination for Autonomous Machine Learning Experiment Selection
Benchmark gains are often mechanism-ambiguous: reproducing an improvement does not by itself identify why it occurs. We study finite-library mechanism discrimination, where posterior-weighted candidate mechanisms, executable probes, and a limited experimental budget define a sequential experiment-se
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