August 24, 2025Open Access

Orchestrated multi agents sustain accuracy under clinical scale workloads compared to a single agent

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Orchestrated multi-agent systems maintained high accuracy in clinical workloads, suggesting efficiency in task management.

Multi-agent systems achieved 90.6% accuracy at 5 tasks, while single agents dropped to 16.6% at 80 tasks.
The study found significant performance differences in task management, with FDR-adjusted p < 0.01 after 10 tasks.
Analysis demonstrated that using multi-agent execution reduced token usage by up to 65-fold, improving efficiency.
This research highlights the importance of orchestration in preserving performance across diverse language models.

Omar et al. (2025) studied this question.

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August 24, 2025Open Access

View Full Paper

Ask AI

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Authors

Orchestrated multi-agent systems maintained high accuracy in clinical workloads, suggesting efficiency in task management.

Multi-agent systems achieved 90.6% accuracy at 5 tasks, while single agents dropped to 16.6% at 80 tasks.
The study found significant performance differences in task management, with FDR-adjusted p < 0.01 after 10 tasks.
Analysis demonstrated that using multi-agent execution reduced token usage by up to 65-fold, improving efficiency.
This research highlights the importance of orchestration in preserving performance across diverse language models.

Omar et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark