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English(EN) DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration

DART框架使用DAG和区块链实现可信的LLM多智能体协作

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出DART，一个旨在增强大型语言模型（LLM）多智能体系统中信任和问责制的新框架。DART利用有向无环图（DAG）结构进行工作流编排，并将其与区块链赋能的去中心化治理相结合。通过实施声誉感知任务分配和动态行为更新，该方法旨在缓解开放环境中不合作或恶意智能体带来的问题。评估表明，DART显著提高了任务成功率，并能有效隔离和遏制恶意智能体行为，其表现优于传统的中心化系统。 AI

影响 增强了多智能体LLM系统中的信任和安全性，可能实现更复杂、更可靠的AI协作。

排序理由 该集群描述了一篇介绍LLM多智能体协作新框架的最新研究论文。

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 →

DART框架使用DAG和区块链实现可信的LLM多智能体协作

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Manoj Kumala, Xinyun Liua, Ronghua Xu ·

    DART：一个基于DAG的声誉和激励框架，通过区块链赋能的治理实现值得信赖的LLM多智能体协作

    arXiv:2609.05529v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for agent reliability, participation, and system-level behavioral alignment. These …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Ronghua Xu ·

    DART：基于区块链赋能治理的 DAG 基础声誉与激励框架，用于可信赖的 LLM 多智能体协作

    Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for agent reliability, participation, and system-level behavioral alignment. These shortcomings leave open environments severely vuln…

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