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.

Empirical Verification: All benchmark claims must be reproducible on specified GPU configurations.
Mathematical Rigor: Complete notation for loss functions, matrix dimensions, and algorithmic steps.
Open Source Code: Public GitHub repositories containing working dependencies and setup guides.