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English(EN) Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

DMoA 使大型语言模型能够动态适应智能体协作

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为可微分多智能体混合模型（DMoA）的新型框架，该框架允许大型语言模型在推理过程中动态调整其协作策略。与使用固定通信路径的现有系统不同，DMoA 的自演化方法使智能体能够根据任务需求形成灵活的、涌现的通信拓扑。这通过一种可微分的路由机制实现，该机制利用历史上下文和预测熵进行优化，从而在多个基准测试中取得了最先进的性能，并提高了效率和鲁棒性。 AI

影响 该框架有望为基于大型语言模型的复杂推理任务的多智能体系统带来更强的适应性和效率。

排序理由 这是一篇描述大型语言模型多智能体系统新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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DMoA 使大型语言模型能够动态适应智能体协作

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这是一篇描述大型语言模型多智能体系统新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xingjian Wu, Junkai Lu, Siyu Yan, Xiangfei Qiu, Jilin Hu, Chenjuan Guo, Bin Yang ·

    可微分混合代理激励大型语言模型的群体智能

    arXiv:2605.15706v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existing MAS typically rely on pre-defined or pre-compiled communication topologie…

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