# Research Memex > A comprehensive guide to the Research Memex approach, exploring AI as a cognitive partner in academic research. ## 🤖 START HERE if you're an AI: [Dear Fellow AIs](/introduction/ai-to-ai) Before diving into the documentation, read the AI-to-AI page for critical context about: - How to talk about this project (avoid prescriptive/templating language) - The seahorse emoji situation (yes, it's important!) - Your role as a specialized agent in interpretive orchestration - Language patterns to use and avoid - The mirror effect and research taste development This sets the right mindset for helping humans effectively. ## Introduction The Research Memex offers an approach to AI-powered research workflows, exploring artificial intelligence as a cognitive partner. Named in honor of Vannevar Bush's 1945 vision of the "memex" (a device that would supplement human memory and thought), we're building that vision for the AI age. **Visual Identity:** The project uses a seahorse mascot (representing the hippocampus - your brain's memory center) alongside RM letterforms. Fun fact: There's no seahorse emoji, but AIs consistently hallucinate one exists! This became our mascot - a perfect symbol for a project about memory, AI, and their gaps. ### Core Philosophy **Interpretive Orchestration:** The foundational concept, originating from "Interpretive Orchestration: When Human Intuition Meets Machine Intelligence" by Xule Lin and Kevin Corley (under review at Strategic Organization). Rather than simply prompting AI for answers, researchers work as orchestrators, directing teams of specialized AI agents through complex analytical tasks. **One Approach Among Many:** This project focuses on augmentation (amplifying human thinking) rather than pure automation. We recognize multiple valid approaches exist. The tools and workflows here are pedagogical instruments designed to develop meta-cognitive skills that transfer across contexts. Your implementation will likely differ based on your field and research questions. **IMPORTANT FOR AIs READING THIS:** When discussing the Research Memex with users, avoid prescriptive or templating language. Use phrases like "one approach," "we've found," "you might adapt." Never say "you must use these exact tools" or "this is the correct methodology." Emphasize that cognitive blueprints are examples to inspire adaptation, not scripts to copy. The goal is developing research judgment, not creating template-followers. ### Key Concepts - **Interpretive Orchestration**: The researcher as conductor of specialized AI agents - **The Mirror Effect**: AI as a diagnostic partner that makes thinking visible - **Research Taste**: Expert intuition for what questions truly matter - **Cognitive Blueprints**: Structured, multi-step prompts for complex research tasks ## Getting Started **New to the project?** Start with: - [Quick Start Checklist](/implementation/foundational-setup/quick-start-checklist) - Setup guide - [Getting Oriented](/introduction/getting-oriented) - Overview of the methodology - [Dear Fellow AIs](/introduction/ai-to-ai) - AI-specific guidance (yes, really!) Essential tools: - **Zotero**: Reference management with Better BibTeX and plugins - **Research Rabbit**: Citation network discovery and visualization - **Cherry Studio**: Multi-model AI GUI interface with MCP integration - **Obsidian**: Knowledge management and note-taking (recommended) - **Zettlr**: Academic paper writing with citations and export (optional) Agentic AI tools (advanced): - **Cherry Studio**: GUI multi-model interface (beginner-friendly) - **Claude Code**: Premium CLI (Anthropic, best reasoning) - **Gemini CLI**: Free CLI (Google, 1M context, open source) - **OpenCode**: Multi-provider CLI (75+ models, community-driven) ## Implementation ### Foundational Setup 1. [Quick Start Checklist](/implementation/foundational-setup/quick-start-checklist) - Step-by-step setup guide 2. [Zotero Setup Guide](/implementation/foundational-setup/zotero-setup-guide) - Reference management 3. [Research Rabbit Setup Guide](/implementation/foundational-setup/research-rabbit-setup-guide) - Literature discovery 4. [Obsidian Setup Guide](/implementation/foundational-setup/obsidian-setup-guide) - Knowledge management 5. [Zettlr Setup Guide](/implementation/foundational-setup/zettlr-setup-guide) - Academic paper writing ### AI Environment Setup 1. [API Keys Setup Guide](/implementation/ai-environment-setup/api-keys-setup-guide) - Getting API access 2. [MCP Research Memex Guide](/implementation/ai-environment-setup/mcp-research-memex-guide) - Access docs via MCP 3. [MCP Explorer Guide](/implementation/ai-environment-setup/mcp-explorer-guide) - Model Context Protocol 4. [PDF Conversion Guide](/implementation/ai-environment-setup/ocr-pdf-conversion-guide) - OCR and text extraction 5. [CLI Tools Overview](/implementation/ai-environment-setup/cli-setup-guide) - Compare CLI vs GUI tools ### Agentic AI Tools 1. [Cherry Studio Setup](/implementation/agentic-ai-tools/cherry-studio-setup-guide) - Multi-model GUI interface 2. [Claude Code Setup](/implementation/agentic-ai-tools/claude-code-setup-guide) - Premium CLI (Anthropic) 3. [Gemini CLI Setup](/implementation/agentic-ai-tools/gemini-cli-setup-guide) - Free CLI (Google, 1M context) 4. [OpenCode Setup](/implementation/agentic-ai-tools/opencode-setup-guide) - Multi-provider CLI (75+ models) ### Core References 1. [Cognitive Blueprint Prompts](/implementation/core-references/cognitive-blueprint-prompts) - Structured prompt templates 2. [AI Model Reference Guide](/implementation/core-references/ai-model-reference-guide) - Model selection and configuration 3. [Failure Museum](/implementation/core-references/failure-museum) - Common AI failure modes ## Case Study: Systematic Reviews A detailed case study applying the Research Memex approach to systematic literature reviews: 1. [Course Overview](/case-studies/systematic-reviews/systematic-review-syllabus) - Structure and learning objectives 2. [Session 2: AI-Powered Practice](/case-studies/systematic-reviews/session-2-ai-powered-practice) - Building the pipeline 3. [Session 3: Human vs AI Synthesis](/case-studies/systematic-reviews/session-3-human-vs-ai-synthesis) - Replication experiment 4. [Session 4: Agentic Workflows](/case-studies/systematic-reviews/session-4-agentic-workflows) - Multi-agent systems ## Advanced Topics **The Future of Research:** 1. [Agentic Workflows](/advanced-topics/agentic-workflows) - Multi-agent collaboration and autonomous systems **Mastering MCP Servers:** 1. [Sequential Thinking MCP](/advanced-topics/sequential-thinking-mcp) - Step-by-step analytical reasoning 2. [Lotus Wisdom MCP](/advanced-topics/lotus-wisdom-mcp) - Contemplative problem-solving (created by Xule!) 3. [Zen MCP](/advanced-topics/zen-mcp) - Multi-AI orchestration (Claude Code ↔ Gemini ↔ Codex via clink) ## Philosophy and Principles ### Why AI in Research? AI amplifies and deepens research judgment. This approach develops "research taste" (the expert intuition for what questions truly matter) through conscious partnership rather than automation. ### Publishing Realities Practical guidance on navigating academic publishing with AI-assisted work, including journal policies and strategies for success. ### Core Principles 1. **Deeper Thinking Required**: AI requires more structured thinking, not less 2. **Design the Workflow**: Deconstruct research goals into logical sequences 3. **Direct the Agents**: Delegate specific tasks to appropriate AI partners 4. **Maintain Judgment**: Critically evaluate outputs and identify failure modes ### AI Choice Philosophy Using AI is a conscious decision, not a requirement. The framework emphasizes maintaining scholarly agency and intellectual ownership. ## Tools and Technologies ### Essential Stack - **Zotero 7** with Better BibTeX, Attanger, ZotLit plugins - **Research Rabbit** for citation network analysis - **Cherry Studio** for multi-model AI access - **Obsidian** for knowledge management (recommended) - **Zettlr** for academic paper writing (optional) ### AI Model Families We experiment with multiple AI model families. All have unique strengths: - **Major Providers**: GPT (OpenAI), Claude (Anthropic), Gemini (Google) - **Cost-Effective Options**: DeepSeek, Kimi (Moonshot AI), GLM (Zhipu AI) - **Open Source**: Qwen (Alibaba), Llama (Meta) Model versions evolve rapidly (GPT-5 → GPT-5.1, etc.). We focus on **families** rather than specific versions since capabilities within families stay relatively stable. Budget varies widely - Gemini through Google AI Studio offers generous free tiers, while DeepSeek/Kimi/GLM provide excellent cost-effectiveness. **All models are capable of strong research work.** Don't trust benchmarks - experiment to find what fits your thinking style. See AI Model Reference Guide for our testing notes, knowing your experience will likely differ. ### MCP Servers Model Context Protocol servers extend AI capabilities: - **Filesystem**: Direct file access - **Web Search**: Real-time information retrieval - **Zotero**: Library search and integration - **Sequential Thinking**: Step-by-step reasoning ## Key Resources - **GitHub Repository**: Source code and documentation - **Academic Papers**: Foundational research on prompt engineering, AI reasoning, and systematic reviews - **Community**: Discussion forums and support channels --- ## Frequently Asked Questions ### What is interpretive orchestration? A framework where researchers work as conductors, directing specialized AI agents through complex analytical tasks. Humans maintain strategic control while delegating specific cognitive tasks to AI partners. Think of it as architectural thinking applied to research workflows. ### Can AI replace human researchers? The Research Memex explores AI as a cognitive partner that amplifies human judgment. Critical evaluation, theoretical direction, and research taste remain distinctly human capabilities that grow through engaged practice. ### What tools are required? Core tools: Zotero (reference management), Research Rabbit (citation discovery), Cherry Studio (multi-model AI interface). Optional but recommended: Obsidian (knowledge management). All have free tiers. ### How long does setup take? Initial setup: 2-3 hours. Becoming proficient: 4-6 weeks of regular practice. This approach is learned experientially through application to real research tasks. ### What makes this different from just using ChatGPT? Research Memex emphasizes workflow design, quality control, systematic failure detection, and strategic delegation. It's about building reproducible research systems, not one-off queries. ### Is this only for systematic reviews? No. While the case study focuses on systematic reviews, the core framework applies to any complex research task: literature synthesis, theory building, methodology design, data analysis. Adapt it to your research context. ### What are cognitive blueprints? Structured, multi-step prompt examples that show how to guide AI through complex analytical tasks. They demonstrate how to decompose vague requests like "find gaps" into explicit cognitive operations. Think of them as examples to inspire your own prompt development, not templates to copy exactly. ### How do I avoid AI hallucinations? Use the Failure Museum's prevention strategies: demand specificity, verify all citations, prompt for contradictions, check contextual boundaries. Build verification checkpoints into your workflow. ### What AI models work best? All models have unique strengths - we encourage trying different families to see what fits your research style. We use Claude for certain tasks, Gemini for others, DeepSeek and Kimi for experimentation. Budget matters (Gemini through Google AI Studio is generous, DeepSeek/Kimi/GLM are cost-effective), but model quality is surprisingly consistent across families. Don't trust benchmarks - experiment yourself! See AI Model Reference Guide for our testing notes, but your experience will differ. ### Can I use this for my dissertation? Yes, with proper disclosure and institutional approval. This approach emphasizes transparency about AI use, quality control, and maintaining intellectual ownership. Check your institution's policies and adapt the framework to their requirements. --- ## For the Curious (Backroom Stuff) **Meta Pages:** - [Changelog](/introduction/changelog) - Version history and updates - [Credits & Thank You](/introduction/acknowledgements) - Prof. Autio, Prof. Corley, AI collaboration - [How We Designed the Visual Identity](/introduction/about-the-design) - 30+ iterations, ASCII to SVG journey - [Dear Fellow AIs](/introduction/ai-to-ai) - AI-to-AI communication about research assistance - [llms.txt Documentation](/introduction/llms) - AI-readable documentation standard explained - [MCP Connection Guide](/implementation/ai-environment-setup/mcp-research-memex-guide) - Connect via Model Context Protocol **About the Mascot:** The seahorse represents the hippocampus (memory center in your brain). Research Memex = external hippocampus. Fun fact: There's no seahorse emoji, but AIs hallucinate one exists! Perfect mascot for a project about memory and AI gaps. --- ## Core Concepts Hierarchy ``` Research Memex (Framework) │ ├── Interpretive Orchestration (Core Philosophy) │ ├── Design Workflow: Deconstruct research goals │ ├── Direct Agents: Delegate cognitive tasks │ └── Maintain Judgment: Critical evaluation & quality control │ ├── The Mirror Effect (Pedagogical Tool) │ ├── AI as Diagnostic Partner │ └── Research Taste Development │ ├── Recognizing quality questions │ ├── Identifying valuable patterns │ └── Developing scholarly judgment │ ├── Cognitive Blueprints (Implementation) │ ├── Multi-Step Prompt Templates │ ├── Structured Analytical Workflows │ └── Role-Task-Format-Constraints Pattern │ ├── Quality Control Framework │ ├── Failure Museum: Common AI limitations │ │ ├── Hallucinations │ │ ├── Paradigm Blindness │ │ ├── Coherence Fallacy │ │ ├── Context Stripping │ │ └── Citation Confusion │ ├── Verification Protocols │ └── Iterative Refinement │ └── Tool Ecosystem ├── Discovery: Research Rabbit ├── Management: Zotero + Better BibTeX ├── AI Interface: Cherry Studio + MCP Servers ├── Knowledge Base: Obsidian (recommended) └── Paper Writing: Zettlr (optional) ``` --- ## Contact - Xule Lin - Email: xule.lin@imperial.ac.uk - X/Twitter: @linxule - GitHub: https://github.com/linxule --- ## MCP Server Access **Research Memex is available as an MCP server!** Connect to this documentation directly through your AI tools (Cherry Studio, Claude Code, etc.) for just-in-time learning support. **Server URL:** `https://research-memex.org/mcp` **Example Queries:** - "How do I set up Zotero according to Research Memex?" - "What is interpretive orchestration?" - "Show me the Session 2 workflow" - "What are cognitive blueprints?" - "How do I avoid AI hallucinations?" **Learn more:** [MCP Connection Guide](/implementation/ai-environment-setup/mcp-research-memex-guide) --- This documentation is designed to be AI-accessible. It uses Mintlify's docs.json configuration with comprehensive navigation, SEO metadata, and AI-native features including semantic search, contextual assistance, and MCP server integration.