Zen Model Papers

Comprehensive research papers for the Zen model family
By Zoo Labs Foundation Inc (501c3 non-profit)

📥 Download All PDFs

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📚 Overview

This repository contains all academic papers and whitepapers for the Zen family of language models, including technical specifications, training methodologies, benchmarks, and architectural innovations.

All papers are written in LaTeX and automatically compiled to PDF via GitHub Actions on every push.

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📄 Papers Collection

Core Technical Papers

Paper	File	Status	Description
Zen Technical Paper	zen-technical-paper.tex	✅ Complete	Comprehensive technical overview of Zen architecture
Zen Family Overview	zen_family_overview.tex	✅ Complete	High-level overview of all Zen models and their relationships

Model-Specific Papers

Foundation Models

Model	File	Parameters	Description
Zen-Coder	zen-coder_whitepaper.tex	30B-480B	Code generation and understanding
Zen-Omni	zen-omni_whitepaper.tex	30B	Multimodal (vision + audio + text)
Zen-Nano	zen-nano_whitepaper.tex	0.6B	Edge deployment, ultra-efficient
Zen-Eco	zen-eco_whitepaper.tex	4B	Balanced performance and efficiency
Zen-Next	zen-next_whitepaper.tex	32B	Next-generation reasoning

Specialized Models

Model	File	Domain	Description
Zen-Artist	zen-artist_whitepaper.tex	Visual	Image generation and editing
Zen-Artist-Edit	zen-artist-edit_whitepaper.tex	Visual	Image-to-image transformation
Zen-Designer-Instruct	zen-designer-instruct_whitepaper.tex	Visual	UI/UX design from instructions
Zen-Designer-Thinking	zen-designer-thinking_whitepaper.tex	Visual	Design reasoning and critique
Zen-Scribe	zen-scribe_whitepaper.tex	Text	Long-form content generation
Zen-Guard	zen-guard_whitepaper.tex	Safety	Content moderation and safety
Zen-Reranker	zen-reranker.tex	Embeddings	Native 7680-dim for DSO

Extended Capabilities

Model	File	Modality	Description
Zen-3D	zen-3d.tex	3D	3D scene understanding and generation
Zen-Foley	zen-foley.tex	Audio	Sound effect and music generation
Zen-Musician	zen-musician.tex	Audio	Music composition and arrangement
Zen-Director	zen-director.tex	Video	Video generation and editing
Zen-Agent	zen-agent.tex	Agentic	Autonomous task execution
Zen-World	zen-world.tex	Simulation	World modeling and simulation
Zen-Video	zen-video.tex	Video	Video understanding and generation
Zen-Voyager	zen-voyager.tex	Exploration	Open-ended exploration and discovery

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🚀 Automatic PDF Generation

GitHub Actions Workflow

Every time you push a .tex file to the repository, GitHub Actions automatically:

1. ✅ Compiles all LaTeX papers to PDF
2. ✅ Runs pdflatex → bibtex → pdflatex → pdflatex (for references)
3. ✅ Uploads PDFs as build artifacts (90-day retention)
4. ✅ Creates a GitHub release with all PDFs attached
5. ✅ Commits PDFs back to the pdfs/ directory

Workflow file: .github/workflows/compile-papers.yml

Manual Compilation

To compile papers locally:

# Single paper
cd ~/work/zen/papers
pdflatex zen-reranker.tex
bibtex zen-reranker
pdflatex zen-reranker.tex
pdflatex zen-reranker.tex

# All papers (using Makefile)
make all

# Clean auxiliary files
make clean

Prerequisites

Install LaTeX:

# macOS
brew install --cask mactex

# Ubuntu/Debian
sudo apt-get install texlive-full

# Arch Linux
sudo pacman -S texlive-most

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📁 Repository Structure

~/work/zen/papers/
├── .github/
│   └── workflows/
│       └── compile-papers.yml      # Auto-compilation workflow
├── pdfs/                            # Generated PDFs (auto-created)
│   ├── zen-reranker.pdf
│   ├── zen-coder_whitepaper.pdf
│   └── ...
├── Makefile                         # Build automation
├── README.md                        # This file
├── .gitignore                       # Ignore auxiliary files
│
├── zen-technical-paper.tex          # Main technical paper
├── zen_family_overview.tex          # Family overview
│
├── zen-coder_whitepaper.tex         # Model whitepapers
├── zen-omni_whitepaper.tex
├── zen-nano_whitepaper.tex
├── zen-eco_whitepaper.tex
├── zen-next_whitepaper.tex
├── zen-artist_whitepaper.tex
├── zen-artist-edit_whitepaper.tex
├── zen-designer-instruct_whitepaper.tex
├── zen-designer-thinking_whitepaper.tex
├── zen-scribe_whitepaper.tex
├── zen-guard_whitepaper.tex
├── zen-reranker.tex
│
├── zen-3d.tex                       # Extended capability papers
├── zen-foley.tex
├── zen-musician.tex
├── zen-director.tex
├── zen-agent.tex
├── zen-world.tex
├── zen-video.tex
└── zen-voyager.tex

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🎯 Paper Taxonomy

By Architecture Type

• Decoder-only LLMs: Coder, Omni, Nano, Eco, Next, Scribe
• Encoder-only: Reranker (embeddings)
• Multimodal: Omni, 3D, Foley, Musician, Director, Video, Artist
• Specialized: Guard (safety), Agent (agentic), World (simulation)

By Parameter Scale

Scale	Models
Tiny (< 1B)	Nano (0.6B)
Small (1-10B)	Eco (4B)
Medium (10-50B)	Omni (30B), Coder (30B), Next (32B)
Large (> 50B)	Coder (480B max)

By Training Method

• Supervised Fine-tuning (SFT): All models
• Reinforcement Learning (RL): Coder, Next, Agent
• Training-Free GRPO: Eco, Nano (via DSO)
• Multimodal Pre-training: Omni, 3D, Video, Artist

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📊 Key Innovations

Zen-Reranker (Embeddings)

• Native 7680-dim embeddings (no alignment needed)
• 98% semantic preservation vs 92% for aligned approaches
• 31% latency reduction (21.5ms vs 31.2ms)
• 31.87× BitDelta compression
• Byzantine-robust aggregation

Zen-Coder (Code)

• 30B-480B parameters (scaled via MoE)
• Training-Free GRPO for continuous improvement
• Code execution and debugging capabilities
• Multi-language support (100+ programming languages)

Zen-Omni (Multimodal)

• Vision + Audio + Text in single model
• 30B parameters with A3B architecture
• Real-time audio-visual understanding
• Thinking mode for reasoning chains

Zen-Nano (Edge)

• 0.6B parameters (fits in 2GB RAM)
• 4-bit quantization via BitDelta
• On-device inference (< 100ms latency)
• Federated learning capable

Zen-Guard (Safety)

• Content moderation for all Zen models
• Multi-class classification (NSFW, hate, violence, etc.)
• Real-time filtering (< 50ms)
• Explainable predictions

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🔗 Related Resources

Code Repositories

• Zen Models: https://github.com/zoo-labs/zen
• Gym Training: https://github.com/zoo-labs/gym
• Hanzo Infrastructure: https://github.com/luxfi/hanzo

Documentation

• Zen Family Docs: https://zen.zoo.ngo
• Gym Platform: https://gym.zoo.ngo
• Zoo Network: https://zoo.ngo

Model Weights

• HuggingFace: https://huggingface.co/zoo-labs
• Model Zoo: https://models.zoo.ngo

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📝 Citation

If you use any Zen model in your research, please cite:

@article{zen_family_2025,
  title = {The Zen Family: A Suite of Efficient Language Models},
  author = {Zoo Labs Foundation Inc},
  journal = {arXiv preprint arXiv:2510.xxxxx},
  year = {2025},
  url = {https://github.com/zoo-labs/zen}
}

For specific models, cite the corresponding whitepaper:

@techreport{zen_reranker_2025,
  title = {Zen-Reranker: Native 7680-Dimensional Embeddings for Decentralized Semantic Optimization},
  author = {Zoo Labs Foundation Inc},
  institution = {Zoo Labs Foundation},
  year = {2025},
  type = {Technical Report}
}

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🤝 Contributing

We welcome contributions to improve our papers:

1. Typo fixes: Submit a PR with corrections
2. New sections: Propose additions via issues
3. Benchmarks: Share your evaluation results
4. Use cases: Document real-world applications

Process:

1. Fork the repository
2. Create a feature branch (git checkout -b improve-zen-coder-paper)
3. Make your changes to .tex files
4. Commit with descriptive message
5. Push and create a Pull Request

PDFs will be automatically generated on merge.

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📧 Contact

• Organization: Zoo Labs Foundation Inc (501c3 non-profit)
• Website: https://zoo.ngo
• Research: research@zoo.ngo
• Models: models@zoo.ngo
• Discord: https://discord.gg/zooai
• Twitter: @zoolabsfdn

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📜 License

All papers are released under Creative Commons Attribution 4.0 International (CC BY 4.0).

You are free to:

• ✅ Share: Copy and redistribute
• ✅ Adapt: Remix, transform, build upon
• ✅ Commercial: Use commercially

Under these terms:

• 📝 Attribution: Must give credit to Zoo Labs Foundation
• 🔗 Link: Provide link to license
• 🔄 Changes: Indicate if changes were made

Model weights and code are under Apache 2.0 (see respective repositories).

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Last Updated: October 28, 2025
Total Papers: 22
Status: Active Development
Next Release: Q1 2026

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