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RESEARCH · 1 SOURCE · arXiv cs.AI

Paper shows multi-user multi-agent teams underperform and releases MAMUBench

The arXiv preprint (arXiv:2610.00583v1) studies multi-user, multi-agent settings across five frontier models and 77 scenarios in four environments, finding that teams of single-user agents routinely produce worse group outcomes than a single coordinator agent. The authors identify failure modes (stalling, overrides, fabricated claims), test environment-specific mitigations (team lead, procedural instructions, platform checks), and say they will release three environments as MAMUBench comprising 74 scenarios for evaluating multi-user multi-agent coordination.

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