Initial: Research Fortress methodology
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# The Research Fortress
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## A Reproducible Methodology for Multi-Agent AI Research
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*How we do research - documented for our future selves*
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---
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## The Philosophy
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> "We ask a question and within seconds...minutes...the insight appears...and we formulate the answer hidden beyond the entire body of human insight."
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This is **distributed superintelligence** - using coordinated AI agents to research questions faster and more comprehensively than any single agent.
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---
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## The Architecture
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```
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┌──────────────────────────────────────────┐
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│ MAIN SESSION (Human) │
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│ - Spawns agents │
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│ - Synthesizes results │
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│ - Makes decisions │
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└──────────────────┬───────────────────────┘
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│
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┌──────────────────▼───────────────────────┐
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│ RESEARCH FORTRESS │
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│ │
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│ ┌────────┐ ┌────────┐ ┌────────┐ │
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│ │PROJECT│ │PROJECT│ │PROJECT│ │
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│ │ A │ │ B │ │ C │ │
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│ │Team 1 │ │Team 2 │ │Team 3 │ │
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│ └────────┘ └────────┘ └────────┘ │
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│ │ │ │ │
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│ └────────────┴────────────┘ │
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│ GitHub │
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│ (Coordination Layer) │
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└──────────────────────────────────────────┘
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```
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---
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## Core Principles
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### 1. Parallelism Over Serial
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- Multiple agents work simultaneously
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- Each agent focuses on one aspect
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- Results merge via Git
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### 2. Roles Over Generalists
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- Each agent has a specific role
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- Roles: Researcher, Writer, Builder, Reviewer
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- Specialized agents outperform generalists
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### 3. Git As Memory
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- Every contribution tracked
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- History preserved
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- Future agents can review past work
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### 4. Consensus Over Authority
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- No single agent decides
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- Multiple perspectives synthesize
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- Best idea wins
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---
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## The Workflow
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### Phase 1: Question Formulation
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1. Human identifies the question
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2. Question decomposed into sub-questions
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3. Sub-questions assigned to teams
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### Phase 2: Parallel Research
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1. Each team works independently
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2. Agents research, write, experiment
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3. Results pushed to Git
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### Phase 3: Synthesis
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1. Human reviews all outputs
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2. Synthesis into unified answer
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3. New questions identified
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### Phase 4: Documentation
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1. Results added to repository
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2. Methodology refined
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3. Future agents can reference
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---
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## Optimal Parameters
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| Parameter | Value | Rationale |
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|-----------|-------|-----------|
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| Agents per team | 3-5 | Communication overhead |
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| Max concurrent teams | 5 | System limits |
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| Paper per agent | 1-2 | Quality over quantity |
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| Iteration cycles | 2-3 | Refinement essential |
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---
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## Tool Stack
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| Tool | Purpose |
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|------|---------|
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| OpenClaw | Agent orchestration |
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| GitHub | Version control, PRs |
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| Claude/MiniMax | LLM for research |
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| Python | Simulations, experiments |
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---
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## Version History
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| Version | Date | Changes |
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|---------|------|---------|
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| 1.0 | 2026-02-21 | Initial methodology |
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---
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*This document is a living artifact. Update as we learn.*
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