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Sparsifying communication topologies in LLM multi-agent systems can lead to significant efficiency gains without sacrificing accuracy, challenging conventional design approaches.
Federated learning can outperform centralized models in multi-agent safety without compromising data privacy, achieving a remarkable 43% reduction in attack success rates.
