Language emergence studies have explored interaction among agents in a network, using a game-theoretic approach (e.g., Lewis signaling games) and reinforcement learning frameworks. Prior research has demonstrated that emergent languages exhibit compositionality (Chaabouni et al., 2020), linguistic conventions shaped by network structure (Lipowska & Lipowski, 2018), and population-driven changes such as improved generalization due to cultural transmission (Cogswell et al., 2019). However, these studies make use of unrealistic tasks and unrealistic agents incapable of reproducing natural language interactions. Recent advancements have expanded multi-agent modeling with large language models capable of reproducing natural language for a range of domains and tasks, including negotiation, consensus seeking, and problem-solving (Guo et al., 2024; Sun et al., 2024). In spirit of this work, I am brainstorming ideas for a project: I am curious to investigate language contact in a multi-agent setting with agents as language models that interact using natural language. I am interested in whether (1) agents develop hybrid languages similar to language change induced by contact among humans, (2) their communication strategies shift toward simplification or complexity over time, and (3) network topology influences linguistic change. This is a nascent idea, so all kind of suggestions are welcomed.