Reimagining Peer Review Process Through Multi-Agent Mechanism Design

2026-01-27

AI Summary

Peer review is confronting a systemic crisis driven by surging submission volumes, misaligned incentives, and reviewer fatigue, leading to declining review quality. This work proposes a novel, incentive-compatible reviewing paradigm that uniquely integrates mechanism design with multi-agent reinforcement learning (MARL). The framework restructures the peer review process through a credit-driven submission economy, MARL-optimized reviewer assignment, and a hybrid validation mechanism, aiming to achieve sustainability, scalability, and fairness. The study further develops a comprehensive architecture encompassing a threat model, fairness guarantees, and phased evaluation metrics, offering both theoretical foundations and practical pathways for the academic community to reform peer review.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The software engineering research community faces a systemic crisis: peer review is failing under growing submissions, misaligned incentives, and reviewer fatigue. Community surveys reveal that researchers perceive the process as"broken."This position paper argues that these dysfunctions are mechanism design failures amenable to computational solutions. We propose modeling the research community as a stochastic multi-agent system and applying multi-agent reinforcement learning to design incentive-compatible protocols. We outline three interventions: a credit-based submission economy, MARL-optimized reviewer assignment, and hybrid verification of review consistency. We present threat models, equity considerations, and phased pilot metrics. This vision charts a research agenda toward sustainable peer review.

Problem

• peer review
• incentive misalignment
• reviewer fatigue
• submission overload
• systemic crisis

Innovation

• multi-agent reinforcement learning
• incentive-compatible mechanism
• peer review system
• credit-based submission economy
• reviewer assignment