AI Co-Scientist on AIBussin — AI applications, systems and books

Appendix 15: Paper Coverage Report: AI as a Co-Scientist

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
    subgraph Paper_Implementation
        P1[Hypothesis Generation<br>GenerationAgent + DSPy]
        P2[Self-Critique / Reflection<br>ReflectionAgent]
        P3[Ranking / Evaluation<br>ELO-style pairwise comparison]
        P4[Evolution & Grafting<br>EvolutionAgent]
        P5[Meta-Review / Summary<br>MetaReviewAgent]
        P6[Vector Memory<br>PostgreSQL + pgvector]
        P7[Embeddings<br>nomic-embed-text via Ollama]
        P8[Traceability<br>CLI + DB logging]
        P9[YAML Pipeline Config<br>Modular control]
        P10[Tooling & CLI<br>ELO visualization]
    end

    subgraph Extensions
        E1[DSPy Integration<br>Composable Signatures & Modules]
        E2[Grafting Logic<br>Merge similar hypotheses]
        E3[Full Pipeline CLI<br>End-to-end execution]
        E4[ELO Visualization<br>Ranking evolution view]
        E5[Local-first Design<br>100% local: Ollama + pgvector]
        E6[Extensible Memory Interface<br>Swap backends]
        E7[Meta Reasoning Hooks<br>Teleprompter & critic ready]
    end

    Start([📄 Paper: AI as a Co-Scientist]) --> Paper_Implementation
    Paper_Implementation --> Extensions
    Extensions --> Future[🔬 Further Research Areas]

    Future --> F1[Teleprompter Optimization]
    Future --> F2[Multi-run Feedback Loops]
    Future --> F3[Experiment Tracking]
    Future --> F4[Evaluation Benchmarks]
    Future --> F5[RAG Integration]
    Future --> F6[Domain-Specific Models]
    Future --> F7[Multi-Agent Dialogues]
    Future --> F8[Exportable Reports]
    Future --> F9[UI / Dashboard]
    Future --> F10[Security / Logging]

    style Start fill:#e1f5fe,stroke:#333
    style Paper_Implementation fill:#fff3e0,stroke:#333
    style Extensions fill:#e8f5e9,stroke:#333
    style Future fill:#fce4ec,stroke:#333

✅ What We’ve Covered from the Paper

(Based on [“AI as a Co-Scientist” — 2502.18864v1])

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

You:

Building an AI Co-Scientist

Thu, 15 May 2025 09:23:16 +0100

This is the first post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🧾 Summary

In this post, I’ll walk through how I implemented the ideas from
AI Co-Scientist: Towards an AI Co-Scientist into a working system called Stephanie.