Position: The Pre/Post-Training Boundary Should Govern IP in Industry-Academia ML Collaborations
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Bergemann, Dirk Ghili, Soheil Mekel-Bobrov, Nitzan |
| author_facet | Bergemann, Dirk Ghili, Soheil Mekel-Bobrov, Nitzan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22632 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Position: The Pre/Post-Training Boundary Should Govern IP in Industry-Academia ML Collaborations Bergemann, Dirk Ghili, Soheil Mekel-Bobrov, Nitzan General Economics Economics 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. |
| title | Position: The Pre/Post-Training Boundary Should Govern IP in Industry-Academia ML Collaborations |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2605.22632 |