Selection Bottleneck Identified as Key Factor in Multi-Agent LLM Pipelines
A new preprint identifies a 'selection bottleneck' in multi-agent large language model (LLM) pipelines, demonstrating that the quality of the selection mechanism can be more critical than team diversity for output quality. In experiments across 42 tasks, judge-based selection achieved a 0.810 win rate against a single-model baseline, while homogeneous teams performed near chance (0.512). The study suggests that improving the selector may have a greater impact than increasing generator diversity in such pipelines.
Why it matters: This work challenges the common assumption that diversity is always beneficial in multi-agent LLM systems, highlighting the decisive role of aggregation methods in pipeline performance.
Full story at: arXiv Multiagent Systems ↗