Episode Details

This paper proposes a new contractual framework called PBOS to resolve persistent intellectual property conflicts in industry-academia machine learning collaborations. By involving scientists in legal negotiations, the authors suggest a clear division based on the pre/post-training boundary of a model. Under this model, pre-training artifacts such as code and architectures are treated as open science, while post-training weights derived from proprietary data remain protected corporate assets. This approach ensures researchers can fulfill academic publication requirements without compromising a company's competitive advantage. Ultimately, the framework aims to reduce the high transaction costs and legal delays that currently prevent many valuable large-scale research partnerships.