用多智能体系统挖掘文献中的非线性关联，发现传统方法忽略的跨学科交叉点。
A Multi-Agent Rhizomatic Pipeline for Non-Linear Literature Analysis
- 12个智能体分阶段运行，基于德勒兹哲学构建非线性分析流程。
- 自动识别跨领域交汇与研究空白，揭示传统方法遗漏的深层结构。
- 适合需要探索复杂知识网络的研究者，开源可扩展至任意领域。
社会科学中的系统性文献综述普遍采用树状逻辑——层级关键词过滤、线性筛选和分类法，压抑了复杂研究图景中横向连接、断裂与涌现模式的本质特征。本文提出基于德勒兹过程关系本体论的多智能体计算管道：根茎式研究代理（V3），由12个专业化智能体在七阶段架构中协同运作。该系统响应Narayan2023年博士研究中使用根茎式探究可持续能源转型的奠基工作，但其依赖人工探索的局限。根茎式研究代理将根茎的六个原则——连接、异质性、多元性、无意义断裂、制图与拓印——转化为自动化流程，集成大语言模型调度、来自OpenAlex与arXiv的双源语料摄入、SciBERT语义拓扑与动态断裂检测协议。初步部署显示，系统能有效发现跨学科汇聚与结构性研究缺口，而这些是传统综述方法系统性忽略的。该管道为开源且可扩展，适用于任何需非线性知识映射的现象领域。
原文摘要 · Abstract (English)
Systematic literature reviews in the social sciences overwhelmingly follow arborescent logics -- hierarchical keyword filtering, linear screening, and taxonomic classification -- that suppress the lateral connections, ruptures, and emergent patterns characteristic of complex research landscapes. This research note presents the Rhizomatic Research Agent (V3), a multi-agent computational pipeline grounded in Deleuzian process-relational ontology, designed to conduct non-linear literature analysis through 12 specialized agents operating across a seven-phase architecture. The system was developed in response to the methodological groundwork established by (Narayan2023), who employed rhizomatic inquiry in her doctoral research on sustainable energy transitions but relied on manual, researcher-driven exploration. The Rhizomatic Research Agent operationalizes the six principles of the rhizome -- connection, heterogeneity, multiplicity, asignifying rupture, cartography, and decalcomania -- into an automated pipeline integrating large language model (LLM) orchestration, dual-source corpus ingestion from OpenAlex and arXiv, SciBERT semantic topography, and dynamic rupture detection protocols. Preliminary deployment demonstrates the system's capacity to surface cross-disciplinary convergences and structural research gaps that conventional review methods systematically overlook. The pipeline is open-source and extensible to any phenomenon zone where non-linear knowledge mapping is required.
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