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Electing leaders boosts LLM social welfare by 55% and survival time by 129% in simulated resource management scenarios, suggesting a surprisingly effective mechanism for improving multi-agent cooperation.
A new family of sparse Mixture-of-Experts models, Arcee Trinity, achieves stable training at scale thanks to a novel MoE load balancing strategy (SMEBU).
