Position: The Pre/Post-Training Boundary Should Govern IP in Industry-Academia ML Collaborations

TL;DR

The paper proposes a clear legal boundary for industry-academia ML collaborations, advocating for pre-training artifacts to be open science and post-training artifacts as business IP, to resolve collaboration tensions.

Contribution

It introduces PBOS, a contract template based on a technically grounded boundary, enabling better legal and scientific alignment in ML collaborations.

Findings

PBOS boundary is technically meaningful and legally auditable.

Adopting PBOS can resolve incentive misalignments in collaborations.

The boundary is grounded in the nature of ML artifacts pre- and post-training.

Abstract

Industry-academia ML collaborations routinely fail to launch -- not for scientific reasons, but because academics must publish while companies must protect models trained on proprietary data, and no standard contract framework resolves this tension. Because contracts are negotiated by legal departments alone, many apparent legal disputes are incentive misalignment problems that only scientists at the table can correctly diagnose. We propose PBOS (Protect-the-Business / Open-Source-the-Science), a community-adoptable contract template anchored to a single technically-grounded boundary: pre-training artifacts (architectures, training code, benchmarks, untrained weights) are open science; post-training artifacts (weights trained on proprietary data) are business IP. This boundary is technically meaningful, legally clean, and auditable -- and could not have been drawn correctly without scientists at the negotiating table. We argue the ML community should adopt PBOS as its default contract for such collaborations.