# Reproducibility is the New Copyleft: Defining AGI-oriented Reproducible Builds

arXiv: https://arxiv.org/abs/2606.03019 · HTML: https://mlsanity.com/paper/2606.03019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Poisoning Web-Scale Training Datasets is Practical](https://mlsanity.com/paper/2302.10149/) (2023)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Towards Training Reproducible Deep Learning Models](https://mlsanity.com/paper/2202.02326/) (2022)
- [The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence](https://mlsanity.com/paper/2403.13784/) (2024)
- [LLM-42: Enabling Determinism in LLM Inference with Verified Speculation](https://mlsanity.com/paper/2601.17768/) (2026)
- [Reproducible Builds: Increasing the Integrity of Software Supply Chains](https://mlsanity.com/paper/2104.06020/) (2021)
- [Several Issues Regarding Data Governance in AGI](https://mlsanity.com/paper/2508.12168/) (2025)

## Abstract (arXiv)

Copyleft, as implemented in licenses such as the GNU General Public License, was a legal hack that used copyright to guarantee user freedom by tying the availability of source code to every act of distribution. Its normative force rested on an implicit technical premise: that source code and object code stand in a well-defined, humanly auditable, and reproducible relationship. Large language models and, prospectively, Artificial General Intelligence (AGI) systems systematically violate this premise. The artifacts jointly required to reconstruct a model -- code, data, weights, hyperparameters, toolchain, and hardware configuration -- are each subject to independent legal, technical, and economic constraints that no current open-source framework fully resolves. Sufficiently capable AI systems can also rewrite licensed source into functionally equivalent derivatives stripped of their original obligations, a form of laundering against which copyleft has no effective defense. This paper argues that a functional analogue of copyleft for AGI must be grounded not in share-alike clauses over code, but in reproducible builds: a practice guaranteeing bit-exact reconstructability from declared inputs. We review the logic of copyleft, critically examine Maffulli's Second Liberation thesis according to which AI fulfills Stallman's dream, and show that the argument collapses unless AGI systems are themselves reproducible. Drawing on the Open Source AI Definition (OSAID), the Model Openness Framework (MOF), OpenMDW, and deterministic-inference research, we define seven requirements for AGI-oriented reproducible builds. We further argue that the Model Context Protocol (MCP) and analogous AI-to-AI coupling mechanisms constitute a new dynamic linking layer for which copyleft-style licensing is ill-suited, and that Masnick's "protocols, not platforms" framework offers a more promising governance template.