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English(EN) The Coordination Tax and the Heterogeneity Dividend: Break-Even Conditions for Multi-Agent LLM Pipelines

多智能体LLM管道产生“协调税”,抵消成本节约

一篇新的arXiv论文探讨了多智能体大型语言模型(LLM)管道的经济学,引入了“协调税”和“异构红利”的概念。研究发现,尽管将任务路由到更便宜的模型可以降低成本,但模型间通信的开销和增加的token量通常会抵消这些节省。在对Claude Opus 5.5和Haiku 5.5在编码任务上的实验中,发现多智能体管道比单模型调用更慢、更昂贵、准确性更低。该研究表明,为特定任务选择最优模型,而不是将其分解给多个智能体,是一种更具成本效益的方法。 AI

影响 表明当前的多智能体LLM架构在许多任务上可能不如单个、选择得当的模型高效。

排序理由 学术论文,详细介绍了LLM管道的新概念和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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多智能体LLM管道产生“协调税”,抵消成本节约

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学术论文,详细介绍了LLM管道的新概念和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andreas Zettl, Diego Gosmar ·

    协调税与异质性红利:多智能体LLM管道的盈亏平衡条件

    arXiv:2610.37700v1 Announce Type: cross Abstract: Multi-agent LLM pipelines built on interoperability protocols such as the Open Floor Protocol (OFP) or A2A are usually justified on capability grounds, with their runtime cost treated as an overhead of unknown size. Comparing such…