---
title: "Contribute to Commune9 - Write, Benchmark & Build with Us"
description: "Contribute to Commune9. Publish peer-reviewed AI research deep dives, benchmark open-weight machine learning models, and lead workshops with 150+ builders."
canonical: "https://commune9.org/contribute/"
---

# Contribute to Commune9 Research & Community

> Contribute to Commune9. Publish peer-reviewed AI research deep dives, benchmark open-weight machine learning models, and lead workshops with 150+ builders.


## How can researchers and engineers contribute to Commune9?

 
Researchers and engineers can contribute to Commune9 by authoring peer-reviewed technical deep dives for The Archive, publishing open-weight model latency benchmarks on the AI Workbench, leading daily arXiv paper reading discussions, or submitting code improvements to our open-source agent libraries and developer CLI tools on GitHub.

 
 
 
 Contribution Track 
 Required Deliverable 
 Review Channel 
 Recognition 
 
 
 
 
 The Archive Deep Dive 
 Peer-Reviewed Technical Article & Code 
 Double-Blind Review Board 
 Lead Author Bylaw & Global Distribution 
 
 
 AI Workbench Benchmarks 
 Reproducible Latency & Memory Metrics 
 Benchmarking Guild GitHub PR 
 Verified Benchmark Contributor Badge 
 
 
 arXiv Circle Facilitator 
 Paper Summary Notes & Discussion Lead 
 Community Discord Leads 
 Community Fellow Status 
 
 
 Open-Source Tooling 
 GitHub Pull Requests to CLI / Core Libs 
 Maintainer Review & CI Suite 
 Open-Source Maintainer Attribution 
 
 
 
 

 
 
## What is the peer-review process for The Archive publications?

 
The Archive peer-review process evaluates technical submissions on empirical reproducibility, mathematical accuracy, and practical engineering value. Submissions undergo rigorous double-blind review by collective machine learning leads, requiring verified benchmarks, complete mathematical formulations, and runnable PyTorch or CUDA code repositories before final publication and global distribution.

 
 - <strong>Empirical Verification:</strong> All benchmark claims must be reproducible on specified GPU configurations.

 - <strong>Mathematical Rigor:</strong> Complete notation for loss functions, matrix dimensions, and algorithmic steps.

 - <strong>Open Source Code:</strong> Public GitHub repositories containing working dependencies and setup guides.

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*Commune9 - The Premier AI Community & Engineering Collective.*
*Canonical URL: https://commune9.org/contribute/*