这篇论文揭示了多智能体交互的潜在问题，指出交互可能不是提高团队表现的万能钥匙，值得深入探讨。
多智能体LLM交互可能损害而非帮助团队表现。研究发现，当智能体阅读彼此的完整输出时，他们的提案在一轮内收敛，消除了使用多个模型所激发的多样性。独立提案生成可避免这种崩溃。这些结果表明，多智能体性能更取决于交换的信息而非智能体数量，且交互仅在智能体在正确时间共享正确信息时才有所帮助。
The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams
Does multi-agent LLM interaction help or hurt? Some work reports gains from debate (Du et al., 2024), critique loops (Chen et al., 2025), and mixture-of-agents synthesis (Wang et al., 2025), while other work finds that interaction adds cost without improving quality under equal budgets (Tran & Kiela, 2026; Xu et al., 2026; Jarrett et al., 2025), or that independent sampling already captures multi-agent gains (Li et al., 2024). We argue this contradiction partly reflects a missing distinction, because not all multi-agent communication is equal. Different model families find structurally different solutions, but when agents read each other's complete outputs, their proposals converge within one round, erasing the diversity that motivates using multiple models. We call this the interaction tax. We test 11 verifier-scored optimization tasks under matched budgets and find that full-solution interaction is a weak default. Independent proposal generation avoids this collapse. Full-solution interaction mainly makes agents stay close to the first solution they see instead of trying different approaches, and critique helps only if the violated rule is easy for the LLM to find and fix. These results suggest that multi-agent performance depends less on the number of agents than on the information they exchange, and interaction helps only when agents share the right information at the right time.