用大模型分析去中心化与企业型AI治理差异，发现开放治理更促共识。
Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
- 用大模型自动分析治理讨论，结合话题建模与网络结构
- 4323条记录显示两种模式参与不平等程度相似
- 去中心化治理虽分散但议题更集中，适合关注公平标准者
随着智能体协议日益增多，其互操作性标准背后的治理结构仍缺乏实证研究。本文提出一种基于大模型的对比分析流水线，融合自动化标注、神经主题建模与多层网络分析，大规模研究社会技术权力结构。以两种对比鲜明的代理互操作标准为例：无许可链上标准ERC-8004与企业主导的Google A2A。分析4,323条治理参与记录，结合大模型辅助编码、主题建模与多层网络分析，探究制度设计如何影响议题重点与社区结构。结果表明，尽管治理形式影响实质关注点，但两种模式均呈现相似程度的参与不平等与社区碎片化。开放治理环境下话语对齐更紧密，说明即使参与分散，开放治理仍可能促进更高程度的议题共识。研究展示了大模型辅助方法在技术治理实证研究中的潜力，对设计更公平的智能体AI标准具有启示意义。所有数据与代码均已公开。
原文摘要 · Abstract (English)
As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures at scale. We validate it on two contrasting standards for agent interoperability: ERC-8004 (permissionless, on-chain) and Google A2A (corporate-led). Analyzing 4,323 governance participation records, we combine LLM-assisted coding, topic modeling, and multi-layer network analysis to examine how institutional design shapes thematic priorities and community structure. We find that while governance form influences substantive focus, both regimes exhibit comparable levels of participation inequality and community fragmentation. Discourse alignment is denser in the permissionless setting, suggesting that open governance may foster greater thematic convergence despite decentralized participation. These findings illustrate how LLM-assisted methods can advance the empirical study of technology governance, with implications for designing more equitable agentic AI standards. All data and code are openly available.
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