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Welcome to a New Way of Thinking About Research

Imagine having a cognitive partner that helps you navigate hundreds of papers, spot patterns across theoretical frameworks, and amplify your analytical capacity while you maintain complete intellectual ownership and develop deeper research judgment. We call this the “Research Memex.” It’s an approach to academic research that reimagines what becomes possible when human and machine intelligence collaborate. We’re discovering how to build a new cognitive environment where AI amplifies our thinking at a scale and depth we’re still exploring. This site shares the philosophy, tools, and workflows we’ve developed. The core framework (interpretive orchestration, cognitive blueprints, quality control) adapts across research domains. Our implementation examples focus on systematic literature reviews, developed through our work in organizational research, though the principles transfer broadly.

Quick Start

Get up and running with the Research Memex approach

Core Philosophy

Understand the principles behind interpretive orchestration

Case Studies

See the approach applied to systematic reviews

Advanced Topics

Explore agentic workflows and MCP servers

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What is a Research Memex?

In 1945, Vannevar Bush imagined the “memex,” a device that would act as an intimate supplement to human memory and thought, helping us navigate and connect ideas across vast knowledge landscapes. We’re building that vision for the AI age. The Research Memex creates a space where AI becomes a genuine partner in the research process, amplifying your intellect and intuition while maintaining rigorous scholarly practice.

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Core Philosophy: Interpretive Orchestration

The central methodology of the Research Memex is interpretive orchestration. Rather than simply prompting AI for answers, we work as orchestrators, directing teams of specialized AI agents through complex analytical tasks.
👤 HUMAN RESEARCHER (Orchestrator)
    |
    +-- 🎯 DESIGN Workflow
    |   (Deconstruct research goals)
    |
    +-- 🎭 DIRECT Agents
    |   (Delegate specific tasks)
    |
    +-- ✓ MAINTAIN Judgment
        (Critical evaluation)
        |
        v
    🤖 AI AGENT TEAM
        - 🔍 Discovery Agent (Literature search)
        - 📊 Analysis Agent (Pattern recognition)
        - 🧠 Synthesis Agent (Theory building)
        - 🎓 Critique Agent (Quality control)
        |
        v
    🔧 RESEARCH TOOLS (Examples)
        - 📚 Reference Management (Zotero, EndNote, etc.)
        - 🕸️ Citation Discovery (Research Rabbit, Connected Papers)
        - 🤖 AI Interface (Cherry Studio, Claude Code, Gemini CLI)
        - 📝 Knowledge Base (Obsidian, Notion, Zettlr)
        |
        v
    📄 RESEARCH OUTPUT
        (Synthesis, Papers, Insights)
        |
        +-- Feedback Loop --> back to Human Researcher
Using ASCII Diagrams: You can copy this diagram directly from the code block and paste it into AI chat sessions, documentation, or text files. It’s fully readable in any monospace environment and works great for explaining your research workflow to AI agents!
This approach requires deeper research thinking. As orchestrators, we:
  • Design the Workflow: Deconstruct complex research goals into logical sequences of analytical steps
  • Direct the Agents: Delegate specific cognitive tasks to appropriate AI partners
  • Maintain Judgment: Critically evaluate AI outputs, identify failure modes, and maintain coherent theoretical direction

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The Mirror Effect: AI as a Diagnostic Partner

A key pedagogical insight of this approach is the “mirror effect.” We use AI as a diagnostic partner that makes our thinking visible and, therefore, improvable.When a vague prompt like “find gaps in the literature” yields a generic, unhelpful response, it reveals a gap in our own structured thinking. This immediate feedback loop creates deeper engagement with the material and helps us develop what we call “research taste”, the expert intuition for what questions truly matter.

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Case Study: Systematic Reviews

To make these concepts concrete, this site includes a detailed case study applying the Research Memex approach to conducting systematic literature reviews. This case study was originally developed for an MRes course (2025) under the guidance of Prof. Erkko Autio and Prof. Kevin Corley at Imperial Business School, Imperial College London. Explore the Case Study → Full acknowledgements →

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Contact

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About the Visual Identity

RM Logo

RM Letterforms

Seahorse Mascot

Seahorse Mascot

Why a seahorse? The hippocampus (Greek: hippos “horse” + kampos “sea monster”) is the memory center of your brain. The Research Memex is your external hippocampus, an external memory system for navigating vast research landscapes.The RM letterforms use bold, blocky typography inspired by ASCII art and terminal interfaces, honoring Vannevar Bush’s 1945 typewriter era while remaining readable in modern contexts. Both elements exist as SVGs for the web and as copy-pasteable ASCII art for terminals, code, and AI chats.Fun fact: There’s no seahorse emoji, but AIs (including the Claude writing this!) consistently hallucinate that one exists, offering 🌊🐙🦈🐚 instead. A fitting mascot for a project about memory, artificial intelligence, and the gaps between them.Learn about the design journey →
Built with ❤️ for researchers · Open source under MIT License
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