Research: From Paper to Pipeline with AI

Fri, 28 Aug 2026 10:00:00 +0100

“Research is formalized curiosity. It is poking and prying with a purpose.”

Summary

There is still something powerful about picking up a research paper and asking:

What can I build from this?

That question turns reading into motion. A paper stops being a sealed object. It becomes a source of ideas, mechanisms, tests, questions, and possible systems.

But the modern version of this workflow is more disciplined than the old one.

Appendix 16: Implementing AI as a Co-Scientist: A Research Case Study

Fri, 28 Aug 2026 10:00:00 +0100

Based on:
van der Wal, R., et al. (2024). AI as a Co-Scientist: Hypothesis Generation and Evaluation with LLMs. arXiv:2502.18864v1

    flowchart TD
    Start([📄 Paper: AI as a Co-Scientist]) --> A[📋 Define Stack<br>DSPy, Haystack, pgvector, Ollama, PostgreSQL]
    A --> B[🏗️ Scaffold Project Structure]
    B --> C[🧠 Create BaseAgent Interface]
    C --> D[🔧 Implement Core Agents]
    
    D --> D1[GenerationAgent<br>DSPy GenerateSignature]
    D --> D2[ReflectionAgent<br>DSPy ReflectSignature]
    D --> D3[RankingAgent<br>ELO + JudgeSignature]
    D --> D4[EvolutionAgent<br>EvolveSignature + Grafting]
    D --> D5[MetaReviewAgent<br>MetaReviewSignature]
    
    D1 --> E[🧩 Define Hypothesis Dataclass]
    D2 --> E
    D3 --> E
    D4 --> E
    D5 --> E
    
    E --> F[💾 Implement VectorMemory<br>BaseMemory + pgvector]
    F --> G[🔍 Add WebSearchTool<br>DuckDuckGo search]
    G --> H[⚙️ Build PipelineRunner<br>YAML config-driven]
    H --> I[📊 Add CLI Viewer<br>Ranking trace + ELO evolution]
    I --> J[✅ End-to-End Pipeline]
    J --> K[🧪 Test Suite<br>Pytest for all components]

    style Start fill:#e1f5fe,stroke:#333
    style A fill:#fff3e0,stroke:#333
    style B fill:#e8f5e9,stroke:#333
    style C fill:#fce4ec,stroke:#333
    style D fill:#f3e5f5,stroke:#333
    style D1 fill:#fff9c4,stroke:#333
    style D2 fill:#e0f7fa,stroke:#333
    style D3 fill:#c8e6c9,stroke:#333
    style D4 fill:#ffccbc,stroke:#333
    style D5 fill:#d1c4e9,stroke:#333
    style E fill:#b2dfdb,stroke:#333
    style F fill:#ffcdd2,stroke:#333
    style G fill:#e1f5fe,stroke:#333
    style H fill:#fff3e0,stroke:#333
    style I fill:#e8f5e9,stroke:#333
    style J fill:#c8e6c9,stroke:#333
    style K fill:#4caf50,stroke:#333,color:#fff

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Research: From Paper to Pipeline with AI

Summary

Appendix 16: Implementing AI as a Co-Scientist: A Research Case Study