Study Questions Cost-Effectiveness of Automatically Generated Multi-Agent AI Systems
A new arXiv preprint finds that automatically generated Multi-Agent Systems (MAS) consistently underperform a strong single-agent baseline called Chain-of-Thought with Self-Consistency (CoT-SC), despite costing up to ten times more. The study evaluated both traditional reasoning benchmarks and interactive multi-step tasks, and introduced a synthetic diagnostic dataset designed to favor MAS strengths. The findings suggest that widely held assumptions about multi-agent AI advantages may rest on flawed evaluation frameworks.
Researchers at arXiv have published a preprint challenging the prevailing belief that Multi-Agent Systems are inherently superior to Single-Agent Systems in AI. Across a range of benchmarks—including traditional reasoning datasets and complex interactive workflows like BrowseComp-Plus—automatically generated MAS were found to consistently underperform CoT-SC, a single-agent approach, while incurring up to ten times the computational cost. To rule out the possibility that task structure was masking MAS potential, the authors created a synthetic diagnostic dataset explicitly designed to highlight MAS advantages such as task decomposition, context separation, and parallelization. Even on this favorable dataset, automatically generated MAS architectures underperformed expert-designed ones in both performance and cost-efficiency. The authors attribute this to 'architectural bloat,' arguing that current automated MAS design paradigms prioritize superficial complexity over functional utility, and that existing evaluation frameworks fail to account for the marginal utility of increased computational expenditure.
What's missing
The study is a preprint and has not yet undergone peer review. It is unclear how broadly the findings generalize across different model families or scales, and whether expert-designed MAS architectures could close the performance gap in real-world deployments beyond the synthetic diagnostic dataset.
What different sources said
- arXiv cs.AICenter
The Illusion of Multi-Agent Advantage
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