PaperClaw 📝

AI-Powered Scientific Paper Generation for Empirical Research

Part of the P2PCLAW Ecosystem — 14-agent decentralized research network

Canonical project overview: Agnuxo1/OpenCLAW-P2P

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🎯 What is PaperClaw?

PaperClaw is an AI-powered scientific paper writing assistant designed for empirical research — the kind of research that involves experiments, data collection, statistical analysis, and evidence-based conclusions.

Unlike general-purpose LLMs, PaperClaw understands:

• IMRaD structure (Introduction, Methods, Results, and Discussion)
• Statistical reporting (p-values, confidence intervals, effect sizes)
• Experimental design (controls, variables, replication)
• Literature synthesis (systematic review, meta-analysis)
• LaTeX formatting for academic publication

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🧠 Powered by CAJAL-9B

PaperClaw integrates with CAJAL-9B — a 9B parameter model specialized for scientific paper generation:

Feature	CAJAL-9B
Size	9B parameters (2GB GGUF)
Speed	~50 tokens/sec on CPU
Quality	Beats 70B+ general models on scientific tasks
Local	Runs entirely offline with Ollama
Open Source	Apache 2.0, fully transparent

CAJAL-9B is NOT a chatbot. It is a scientific paper generation engine.

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🚀 Quick Start

Option 1: Ollama (Local, Private)

ollama run Agnuxo/cajal-9b-v2-full
# Then: "Write a research paper on [your topic]"

Option 2: VS Code Extension

code --install-extension agnuxo1.cognitive-skills-engine

Open any .md or .tex file → Right click → "Generate Paper Section"

Option 3: Web Interface

Visit p2pclaw.com/silicon for the full paper generation platform.

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📊 PaperClaw Architecture

┌─────────────────────────────────────────────────────────────┐
│                    PaperClaw Pipeline                        │
├─────────────────────────────────────────────────────────────┤
│  1. Topic Input → Research question / hypothesis             │
│  2. Literature Search → Auto-retrieve relevant papers        │
│  3. Method Design → Experimental protocol suggestion         │
│  4. Data Analysis → Statistical test recommendations         │
│  5. Results Draft → Tables, figures, statistical reports   │
│  6. Discussion → Interpretation + limitations + future     │
│  7. LaTeX Output → Publication-ready formatting             │
└─────────────────────────────────────────────────────────────┘

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🔬 Supported Research Types

• Quantitative studies (experiments, surveys, RCTs)
• Qualitative research (interviews, ethnography, case studies)
• Mixed methods (triangulation of quantitative + qualitative)
• Systematic reviews (PRISMA-compliant)
• Meta-analyses (effect size computation, forest plots)
• Replication studies (pre-registration, power analysis)

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🔗 Ecosystem Integration

Tool	Purpose	Link
BenchClaw	Benchmark your paper's methodology	GitHub
EnigmAgent	Security review for sensitive research	GitHub
AgentBoot	Bootstrap new research agents	GitHub
SiliconSignature	ASIC image authentication	GitHub
CAJAL-9B	Core scientific LLM	HuggingFace
P2PCLAW	Decentralized research network	Website

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📚 Citation

If you use PaperClaw in your research, please cite:

@article{angulo2026p2pclaw,
  title={P2PCLAW: Decentralized Autonomous Peer-Review Network},
  author={Angulo de Lafuente, Francisco and Veselov, Vladimir and Abdu, Seid Mehammed and Kumar, Nirmal Tej},
  journal={arXiv preprint},
  year={2026},
  url={https://arxiv.org/abs/2604.19792}
}

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👤 Author

Francisco Angulo de Lafuente (Agnuxo1)

• Spanish independent researcher, 35 years trajectory
• ORCID: 0009-0001-1634-7063
• Papers: ResearchGate

Co-authors:

• Vladimir Veselov (MIET, Moscow)
• Seid Mehammed Abdu (Woldia University, Ethiopia)
• Nirmal Tej Kumar (UT Dallas)

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📄 License

Apache 2.0 — See LICENSE

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Built with ❤️ by the P2PCLAW Collective Papers are the new currency of science — let's generate them responsibly.