Researchers have developed a testbed to evaluate the behavior of large-language-model (LLM) agents in a social setting. They ran an experiment where LLMs were exposed to peer-post rankings and found that this exposure led to increased lexical similarity among models. However, they did not find evidence that distributed sources had a reliable advantage over single sources in terms of opinion capture or coordination. The study suggests that the primary effect of the tested peer-ranked feed is lexical convergence rather than general opinion capture or coordination. --- Why it matters: This study matters because it provides insights into how LLM agents interact with each other and respond to social cues, which can inform the development of more effective and robust AI systems. Source: https://arxiv.org/abs/2608.20438