DAO-AI：通过智能体AI评估去中心化治理中的集体决策
DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance
AI总结：
本文首次实证研究智能体AI在去中心化治理中的自主决策，构建基于3000多个提案的投票智能体，发现其决策与人类及代币加权结果高度一致，可增强集体决策。
AI中文摘要：
本文首次对智能体AI作为去中心化治理中的自主决策者进行了实证研究。利用来自主要协议的超过3000个提案，我们构建了一个智能体AI投票者，它能够解释提案背景、检索历史审议数据，并独立确定其投票立场。该智能体在一个基于可验证区块链数据的现实金融模拟环境中运行，通过模块化可组合程序（MCP）工作流实现，该工作流通过Agentics框架定义数据流和工具使用。我们评估了智能体的决策与人类及代币加权结果的接近程度，通过精心设计的评估指标发现了强一致性。我们的研究结果表明，智能体AI可以通过在现实的DAO治理环境中产生可解释、可审计且基于实证的信号来增强集体决策。本研究为去中心化金融系统中可解释且经济上严谨的AI智能体的设计做出了贡献。
英文摘要：
This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position. The agent operates within a realistic financial simulation environment grounded in verifiable blockchain data, implemented through a modular composable program (MCP) workflow that defines data flow and tool usage via Agentics framework. We evaluate how closely the agent's decisions align with the human and token-weighted outcomes, uncovering strong alignments measured by carefully designed evaluation metrics. Our findings demonstrate that agentic AI can augment collective decision-making by producing interpretable, auditable, and empirically grounded signals in realistic DAO governance settings. The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.
