arXiv — cs.AI preprintsInternational12 October 2026
Do More Agents Help? Evaluating Accuracy and Cost in LLM Multi-Agent Workflows
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arXiv:2606.05670v2 Announce Type: replace Abstract: Choosing whether to replace a single-agent controller requires evidence on both task performance and inference expenditure. We present BenchAgent, a shared evaluation framework, and compare six multi-agent workflow configurations against its single-agent reference, BenchAgent Core, across ten reasoning, coding, and tool-use benchmarks. All configurations use GPT-4.1 and common tool and scoring interfaces; Core executions share the same step limits. We measure accuracy and recorded token use over three runs on fixed task sets, with 3,302 insta
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