An ensemble-based information-theoretic task selection framework makes LLM multi-agent communication-structure optimization beat random training in both benign and adversarial settings
The work proposes an ensemble-based information-theoretic task selection framework that estimates task informativeness by how much a candidate task changes the distribution over graph parameters, using ensemble Kalman inversion as a derivative-free approximation of the Bayesian update, and combines embedding-based representative selection, surrogate modeling, and batch Thompson sampling for scalability; validated across benign and adversarial settings and multiple task formats, it consistently outperforms random training with more effective task selection and greater overall cost efficiency.