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English(EN) Decentralized LLM Workflows via Sequential Auctions

新框架通过顺序拍卖实现去中心化大语言模型任务完成

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个框架，通过一个由自利代理组成的去中心化市场来解决多步任务，超越了假设完美竞争的理论模型。这种新方法研究了异构代理如何在不完全竞争的市场中盈利运营。该研究评估了四种竞标算法——Mirror Descent、Thompson Sampling、Bayes-Optimal 和 PPO——在三种拍卖协议下的表现，证明该模型在保持高任务成功率和降低成本的同时，维持了代理的利润率。 AI

影响 这项研究可能通过去中心化市场实现大语言模型更高效、更具成本效益地执行复杂的多步任务。

排序理由 学术论文，详细介绍了一种用于去中心化大语言模型工作流的新框架。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新框架通过顺序拍卖实现去中心化大语言模型任务完成

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学术论文，详细介绍了一种用于去中心化大语言模型工作流的新框架。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ian Gemp, Shubhendu Trivedi, Adam Fisch, Fantine Huot, Michael Kaisers, Mirella Lapata, William W. Cohen, Alekh Agarwal, Kate Larson, Jacob Eisenstein ·

    通过顺序拍卖实现去中心化LLM工作流

    arXiv:2610.22201v1 Announce Type: cross Abstract: We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstrated that agents organized through sequential auctions can converge to a globall…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jacob Eisenstein ·

    通过顺序拍卖实现去中心化LLM工作流

    We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstrated that agents organized through sequential auctions can converge to a globally optimal policy in a Markov decision process (MDP…

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