Research papers identify widespread homogeneity in LLM multi-agent systems

Multiple studies find that LLM ensembles and multi-agent panels often collapse into a narrow consensus, with 16 models from 10 families producing only 1.69 distinct semantic formulations. This "artificial hivemind" effect limits diversity and undermines the effectiveness of majority voting and debate protocols in safety and reasoning tasks.