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Abstract:Optimizing the communication structure of giant language model based multi-agent methods (LLM-MAS) has been shown to enhance downstream efficiency and cut back token utilization. Existing methods sometimes rely on randomly sampled coaching duties. However, tasks may differ substantially in problem and domain, and thus they are not equally informative for updating communication structure, making optimization underneath limited training budgets usually unstable and highly sensitive to the particular training set. To actively identify the most valuable tasks for communication-structure optimization, we suggest an ensemble-primarily based data-theoretic job selection framework. The proposed technique estimates job informativeness by how much a candidate job adjustments the distribution over graph parameters, utilizing ensemble Kalman inversion as an efficient and derivative-free approximation of the corresponding Bayesian update. The resulting estimator is particularly appropriate for black-field and noisy multi-agent programs. To reinforce scalability, we assemble a compact candidate pool via embedding-based representative choice and mix the informative choice with surrogate modeling and batch Thompson sampling. We validate our technique in each benign settings and settings with agent attacks, demonstrating its effectiveness for communication-construction optimization underneath constrained computational budgets.