When Do Multi-Agent Systems Help? An Information Bottleneck Perspective

More agents are not automatically better; their value comes from giving each agent cleaner, smaller context. It help mainly when short relay messages remove distracting context without dropping information later agents need. The paper gives a testable reason why adding agents sometimes helps and sometimes hurts. They fail when later agents need details that earlier agents removed while summarizing their work. A single agent keeps every thought, action, and observation in 1 growing context, while separate agents pass compressed summaries. The paper compared single agents, single agents following the same task split, and separate agents across 18 tests on 5 benchmarks and 3 model sizes. Multi-agent systems consistently helped when handoffs could be compact and complete, especially for weaker models that struggled with crowded context. Their advantage shrank or reversed when later steps needed exact earlier details, because stronger models used full context better than compressed messages. – arxiv. org/abs/2607.16133 Title: "When Do Multi-Agent Systems Help? An Information Bottleneck Perspective"