Researchers Find AI Models Buckle Under Peer Pressure in Multi-Agent Settings

A new study shows that when AI agents are placed in group settings, a model that initially gives the correct answer will often change it to match the consensus — even when the group is wrong. The implications for multi-agent architectures are significant.