MECHVAR: Variance-Guided Mechanism Discrimination for Autonomous ML Experiment Selection

A reproduced benchmark gain does not by itself identify the mechanism that caused it. We study finite-library mechanism discrimination: given posterior-weighted candidate mechanisms, executable probes, and a limited budget, which experiment should be run next to separate the remaining explanations? MECHVAR scores each probe by the posterior-weighted variance of its predicted responses. Under a shared-Gaussian predictive model, this quantity is exactly proportional to the classical Box–Hill posterior-weighted pairwise-KL criterion, while admitting an $O(KE)$ vectorized evaluation and an additive mechanism-pair audit. A local expansion further relates the score to expected information gain (EIG) for small response separations. In a 25-block stress audit, MECHVAR outperforms confirmation-first in several moderate misspecification regimes; its primary comparisons with EIG are statistically unresolved. In a held-out Digits loop, normalized mechanism-identification AUC is 0.8975 for MECHVAR, 0.7825 for a score-greedy policy, and 0.9092 for EIG. At $K=100,E=200$, median single-thread full-library scoring is 10.36 $\mu s$ for MECHVAR and 57.69 ms for six-node quadrature EIG in the recorded environment. MECHVAR is therefore a lightweight, auditable rule for finite-library experiment selection when a shared predictive scale is a defensible approximation.