Abstract
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.
Abstract (translated)
社会科学中的系统性文献综述几乎普遍遵循树状逻辑——即层级化关键词筛选、线性筛查与分类学归类——这类方法会压制复杂研究领域中特有的横向关联、断裂点与涌现模式。本研究提出块茎研究智能体V3（Rhizomatic Research Agent V3），这是一个基于德勒兹过程关系本体论的多智能体计算流水线，旨在通过横跨七个阶段架构的12个专业化智能体实现非线性文献分析。该系统是针对（Narayan2023）所建立的方法论基础而开发的，后者在其关于可持续能源转型的博士研究中采用了块茎探究法，但依赖研究者手动驱动探索。块茎研究智能体将块茎的六项原则——连接性、异质性、多元性、无意义断裂、制图与转印——转化为自动化流水线，整合了大语言模型（LLM）编排、来自OpenAlex与arXiv的双源语料库摄入、SciBERT语义地形图以及动态断裂检测协议。初步部署表明，该系统能够揭示出传统综述方法系统性地忽视的跨学科聚合点与结构性研究空白。该流水线采用开源设计，可扩展至任何需要非线性知识映射的现象领域。
URL
https://arxiv.org/abs/2603.28336
