pith. sign in

arxiv: 2605.22632 · v1 · pith:NNMKNSGRnew · submitted 2026-05-21 · 💰 econ.GN · q-fin.EC

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

Dirk Bergemann , Soheil Ghili , Nitzan Mekel-Bobrov This is my paper

Pith reviewed 2026-05-22 04:10 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC
keywords machine learningindustry-academia collaborationintellectual propertycontract templatespre-trainingpost-trainingopen science
0
0 comments X

The pith

A pre/post-training boundary resolves IP tensions in industry-academia ML collaborations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Many machine learning collaborations between industry and academia fail to start because academics must publish their work while companies need to safeguard models trained on their private data. The paper identifies that contracts negotiated only by lawyers miss the technical details, and proposes involving scientists to draw a clear line between pre-training elements like model architectures and code, which remain open, and post-training weights from proprietary data, which stay protected. This leads to the PBOS contract template that the authors argue should become the standard for the field. Readers would care if this enables more joint research that benefits both scientific progress and commercial applications.

Core claim

The central claim is that the pre/post-training boundary is technically meaningful, legally clean, and auditable, and that it could not have been drawn correctly without scientists at the negotiating table. The authors propose the PBOS template anchored to this boundary as the community-adoptable default contract for such collaborations.

What carries the argument

The PBOS contract template, which designates pre-training artifacts as open science and post-training artifacts as business IP.

If this is right

  • Collaborations launch more frequently once IP is separated at the training boundary.
  • Scientists must participate in contract negotiations to correctly identify technical boundaries.
  • The ML community can standardize on PBOS to reduce negotiation friction.
  • Apparent legal disputes often stem from incentive misalignments that scientists can diagnose.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This boundary approach might apply to other data-driven research fields with similar IP tensions.
  • Adoption could lead to more open-source contributions from industry-academia projects.
  • Real-world testing of PBOS contracts would show if the boundary holds in practice without new disputes.

Load-bearing premise

IP negotiation is the main barrier to collaborations and the pre/post-training distinction can be clearly defined and enforced without new conflicts.

What would settle it

An observed industry-academia ML project that adopts the pre/post-training boundary but still cannot launch due to unresolved IP issues over the classification of artifacts.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that industry-academia ML collaborations routinely fail to launch due to IP tensions in contracts negotiated solely by legal teams, and proposes the PBOS contract template anchored to a pre/post-training boundary: pre-training artifacts (architectures, training code, benchmarks, untrained weights) are treated as open science while post-training artifacts (weights on proprietary data) are protected business IP. It asserts this boundary is technically meaningful, legally clean, and auditable, and that scientists must participate in negotiations to define it correctly, advocating community adoption as the default framework.

Significance. If the pre/post-training distinction can be made operational without new ambiguities, the position paper identifies a practical barrier to collaboration and offers a concrete, community-adoptable template that could increase successful industry-academia partnerships while preserving publication incentives and model protection. The emphasis on a technically grounded rather than purely legal boundary is a constructive contribution to policy discussions in ML.

major comments (3)
  1. [Abstract] Abstract and proposal section: the assertion that the pre/post-training boundary 'is technically meaningful, legally clean, and auditable' and 'could not have been drawn correctly without scientists' is load-bearing for the recommendation, yet the manuscript provides no analysis of how the boundary applies to continued pre-training, staged fine-tuning, RLHF, or hybrid data mixes where proprietary information enters at multiple stages.
  2. [Proposal for PBOS] Definitions of pre- and post-training artifacts: the listed categories (architectures/training code/benchmarks/untrained weights vs. weights trained on proprietary data) do not specify classification rules for iterative or multi-stage pipelines, directly threatening the claimed enforceability and stability of the boundary.
  3. [Introduction] Central argument on incentive misalignment: the claim that 'many apparent legal disputes are incentive misalignment problems that only scientists at the table can correctly diagnose' rests entirely on assertion without case studies, failed negotiation examples, or legal precedent analysis to demonstrate that scientist involvement would have produced the proposed boundary.
minor comments (2)
  1. [Overall] The manuscript would benefit from an explicit sample PBOS contract clause or template outline to make the proposal more actionable for readers.
  2. [Abstract] Consider adding citations to existing IP frameworks or prior work on ML collaboration contracts to better situate the novelty of the PBOS approach.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our position paper. We agree that the manuscript would benefit from greater specificity on how the pre/post-training boundary operates in complex, multi-stage training scenarios. We respond to each major comment below and indicate the revisions we intend to make in the next version.

read point-by-point responses

  1. Referee: [Abstract] Abstract and proposal section: the assertion that the pre/post-training boundary 'is technically meaningful, legally clean, and auditable' and 'could not have been drawn correctly without scientists' is load-bearing for the recommendation, yet the manuscript provides no analysis of how the boundary applies to continued pre-training, staged fine-tuning, RLHF, or hybrid data mixes where proprietary information enters at multiple stages.

    Authors: We accept this observation and will add a new subsection to the PBOS proposal that explicitly addresses these cases. In continued pre-training or hybrid data mixes, the boundary is defined at the first incorporation of proprietary data; all downstream artifacts inherit post-training status. For staged fine-tuning and RLHF, the pre-training phase concludes prior to any proprietary data or human feedback, with provenance logs used for auditability. This clarification reinforces rather than weakens the claim that scientists are required to identify the correct transition points. We will include concrete examples of classification in the revised text. revision: yes

  2. Referee: [Proposal for PBOS] Definitions of pre- and post-training artifacts: the listed categories (architectures/training code/benchmarks/untrained weights vs. weights trained on proprietary data) do not specify classification rules for iterative or multi-stage pipelines, directly threatening the claimed enforceability and stability of the boundary.

    Authors: The referee correctly notes a limitation in the current definitions. We will expand the definitions section with an explicit classification protocol for iterative and multi-stage pipelines: any weights or derived artifacts that incorporate proprietary data at any training stage are classified as post-training business IP, while base architectures, initial code, and benchmarks remain pre-training open science. A simple decision procedure based on data provenance will be added to support enforceability and long-term stability of the boundary. revision: yes

  3. Referee: [Introduction] Central argument on incentive misalignment: the claim that 'many apparent legal disputes are incentive misalignment problems that only scientists at the table can correctly diagnose' rests entirely on assertion without case studies, failed negotiation examples, or legal precedent analysis to demonstrate that scientist involvement would have produced the proposed boundary.

    Authors: As a position paper, the argument is primarily conceptual and draws on widely observed patterns in the field. To strengthen the presentation, we will insert a short paragraph referencing publicly reported challenges in industry-academia AI collaborations and note that the proposed boundary reflects technical distinctions that are typically outside the expertise of legal teams alone. We do not present original empirical case studies or exhaustive legal precedent analysis, as these fall outside the scope of a position paper; the claim remains that technical input from scientists is necessary to locate the boundary correctly. revision: partial

Circularity Check

0 steps flagged

No significant circularity in policy position paper

full rationale

The paper is a forward-looking position paper proposing the PBOS contract template anchored to a pre/post-training boundary for industry-academia ML collaborations. It presents normative arguments about incentive misalignment, the technical meaning of the boundary (architectures/training code/benchmarks/untrained weights vs. weights trained on proprietary data), and the need for scientists in negotiations. No mathematical derivations, equations, fitted parameters, or self-citations appear in the provided text that reduce the central claim to its own inputs by construction. The assertion that the boundary is 'technically meaningful, legally clean, and auditable' and 'could not have been drawn correctly without scientists' is offered as a direct logical inference from the described problem, not as a self-definitional loop or renamed known result. The paper stands as self-contained policy analysis without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The proposal rests on domain assumptions about contract enforceability and the technical separability of pre- and post-training artifacts rather than on fitted parameters or newly invented physical entities.

axioms (2)
  • domain assumption The pre-training artifacts (architectures, training code, benchmarks, untrained weights) can be cleanly separated from post-training artifacts (weights trained on proprietary data) in practice.
    This separability is invoked as the technically meaningful boundary that makes the contract template workable.
  • domain assumption IP disputes in industry-academia ML collaborations are primarily incentive misalignment problems that scientists can diagnose better than legal departments alone.
    Stated directly in the abstract as the reason contracts fail to launch.
invented entities (1)
  • PBOS contract template no independent evidence
    purpose: To serve as a community-adoptable default contract that implements the pre/post-training IP boundary.
    New named framework introduced to operationalize the proposed boundary.

pith-pipeline@v0.9.0 · 5692 in / 1372 out tokens · 44053 ms · 2026-05-22T04:10:24.096347+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages

  1. [1]

    Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =

    Model Cards for Model Reporting , author =. Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =. 2019 , publisher =

    work page 2019

  2. [2]

    Communications of the

    Datasheets for Datasets , author =. Communications of the. 2021 , publisher =

    work page 2021

  3. [3]

    Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of

    Gokaslan, Aaron and McDuff, Daniel and Korjakow, Tim and Cambo, Scott and Benjamin, Jesse Josua and Lee, Jenny and Jernite, Yacine and Mu. Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of. Proceedings of the 41st International Conference on Machine Learning , series =. 2024 , publisher =

    work page 2024

  4. [4]

    Membership Inference Attacks Against Machine Learning Models , author =. 2017. 2017 , publisher =

    work page 2017

  5. [5]

    Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and van den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and K...

    work page 2016

  6. [6]

    Chowdhery, Aakanksha and Narang, Sharan and Devlin, Jacob and Bosma, Maarten and Mishra, Gaurav and Roberts, Adam and Barham, Paul and Chung, Hyung Won and Sutton, Charles and Gehrmann, Sebastian and others , journal =

    work page

  7. [7]

    Advances in Neural Information Processing Systems , volume =

    Language Models are Few-Shot Learners , author =. Advances in Neural Information Processing Systems , volume =

    work page

  8. [8]

    Do Formal Intellectual Property Rights Hinder the Free Flow of Scientific Knowledge?

    Murray, Fiona and Stern, Scott , journal =. Do Formal Intellectual Property Rights Hinder the Free Flow of Scientific Knowledge?. 2007 , doi =

    work page 2007