Does Multi-Agent Collaboration Pay When Agents Serve Users? Testing Agent Scaling in Dual-Control Settings
Multi-agent systems (MAS) built from language models are deployed on the premise that collaboration beats a single-agent system (SAS), yet when it pays is unclear. The most systematic study to date gives a capability-saturation rule: coordination helps while the SAS is weak and hurts once it is strong, with the crossing near 45% single-agent accuracy. That rule was derived on autonomous benchmarks, where no user takes part. We test it where agents now work: in conversation with a user under a written policy, on $τ^2$-bench, the dual-control benchmark. On its airline, retail and telecom domains we compare the stock SAS with a centralized MAS built from the same model for six models from th...
| Authors | Ethan Yang, Xiangcheng Shi |
|---|---|
| Primary category | cs.AI |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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