What question did this study set out to answer?

This paper explores how AI can transform peer review, addressing structural mismatches between AI capabilities and traditional systems.

April 26, 2026Open Access

AI Peer Review: Founding Position Paper

Key Points

This paper explores how AI can transform peer review, addressing structural mismatches between AI capabilities and traditional systems.
Proposes a four-stage hybrid architecture: AI triage, AI advocate-critic, human editorial decision, mixed re-review.
Uses a structural analogy with MLB's 2026 Automated Ball-Strike system to illustrate insights.
Discusses the importance of explicit epistemic accountability frameworks for AI-assisted review.
Argues that AI-assisted review can create a faster process for reviewers and fairer assessment for authors.
Highlights the potential for enhanced thoroughness in mathematical discovery due to AI integration.
Suggests that implementing this architecture could lead to significant improvements in peer review efficiency.

Abstract

AI-assisted peer review is not merely a response to a capacity crisis — it is an opportunity to make the review process better for everyone: faster for reviewers, fairer for authors, and more complete for the mathematical community as a whole. This founding position paper identifies the structural mismatch between AI-accelerated mathematical discovery and traditional peer review infrastructure, argues that AI-assisted review governed by explicit epistemic accountability frameworks is a win-win-win structural upgrade, and proposes a concrete four-stage hybrid architecture (AI triage → AI advocate-critic → human editorial decision → mixed re-review). An appendix situates the current moment within a 1980–2050 trajectory using MLB's 2026 Automated Ball-Strike system as a structural analogy. The lesson: done right, it makes the game better. Part of the AI Epistemic Governance Stack (Papers 1–10 at Zenodo). This is Paper 11. Related frameworks:- AEAL: 10.5281/zenodo.19565086- ZTEK: 10.5281/zenodo.19591564- AI Behavior Science Founding Paper: 10.5281/zenodo.19562751- Change Management Framework: 10.5281/zenodo.19591685

Read Full Paperexternally

Mark Helpful

Bookmark

Relay

View Full Paper