A Multi-Agent Rhizomatic Pipeline for Non-Linear Literature Analysis
Summary: arXiv:2603.28336v1 Announce Type: new
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 Narayan (2023), 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
- Decalcomania
These principles are integrated into an automated pipeline that employs large language model (LLM) orchestration, dual-source corpus ingestion from OpenAlex and arXiv, SciBERT semantic topography, and dynamic rupture detection protocols. The preliminary deployment of this system demonstrates its capacity to surface cross-disciplinary convergences and structural research gaps that conventional review methods systematically overlook.
Key Features of the Rhizomatic Research Agent
The Rhizomatic Research Agent offers several innovative features that distinguish it from traditional literature review methodologies:
- Multi-Agent Architecture: The system consists of 12 specialized agents that work collaboratively to analyze literature from various angles, ensuring a comprehensive perspective.
- Non-Linear Processing: Unlike traditional linear methodologies, this pipeline allows for non-linear exploration of literature, recognizing the complex interrelations within research topics.
- Dynamic Protocols: The integration of dynamic rupture detection protocols enables the identification of emerging patterns and unexpected connections in the literature, fostering innovative insights.
- Open-Source Accessibility: As an open-source tool, the Rhizomatic Research Agent is extensible to any phenomenon zone where non-linear knowledge mapping is required, promoting collaborative enhancement and adaptation.
Implications for Social Sciences Research
The introduction of the Rhizomatic Research Agent marks a significant advancement in the field of social sciences research. By facilitating non-linear literature analysis, this system encourages researchers to explore beyond traditional boundaries, uncovering novel insights and fostering interdisciplinary collaboration. The potential applications of this tool span various domains, from sustainable energy transitions to cultural studies, where complex interconnections are often overlooked.
In conclusion, the Rhizomatic Research Agent (V3) serves as a pioneering tool for researchers seeking to navigate the intricate landscapes of knowledge within the social sciences. Its innovative approach to literature analysis promises to reshape the way researchers engage with and synthesize existing knowledge, ultimately contributing to the evolution of research methodologies in the 21st century.