这篇论文从信息瓶颈角度讲清楚了多智能体系统在什么时候比单智能体更有优势，还通过18个实验验证了弱模型受益多、强模型受益少。
这篇论文从信息瓶颈视角分析多智能体系统（MAS）与单智能体系统（SAS）的差异。关键发现是：MAS通过有界中继消息连接隔离的局部上下文，而SAS将所有推理轨迹积累在共享上下文中。在无限中继带宽下，任何SAS都可被MAS模拟；但在有限带宽下，压缩引入权衡——减少冗余上下文可提升效率，但可能损失任务相关信息。作者将该权衡形式化为由参数β控制的信息瓶颈问题，并通过18个控制实验（涵盖5个基准和3个模型规模）验证：当中继接近充分时，MAS持续有效（尤其对较弱模型）；当中继造成信息损失时，MAS优势缩小甚至反转（尤其对较强模型）。
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective
LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, their advantages over single-agent systems (SAS) remain unclear, with performance varying inconsistently across settings. Here, we provide an information bottleneck perspective on elucidating the differences between MAS and SAS. Specifically, our key observation is that a SAS accumulates its full reasoning trace in one shared context, while a MAS uses isolated local contexts connected by bounded relay messages. We show that, under infinite relay bandwidth, any SAS can be simulated by a MAS that transmits the full upstream context. Thus, the nontrivial advantage of MAS arises under bounded relays, where compression introduces a fundamental trade-off: reducing redundant context can improve efficiency, but may also incur loss of task-relevant information. We formalize this trade-off as an information bottleneck controlled by an effective parameter $β$, which captures how the balance shifts with model capability, and shows that MAS gains arise when context reduction outweighs relay information loss. We conduct 18 controlled experiments across five benchmarks and three model scales to validate our theoretical studies. We observe that MAS consistently helps when relays are near-sufficient, especially for weaker models. In contrast, MAS gains shrink or reverse when relays incur information loss, especially for stronger models that can already extract useful information from redundant context and thus gain little from compression. Our study shows that multi-agent design is fundamentally an information-bottleneck optimization problem. This perspective explains when bounded inter-agent communication helps or hurts.