# Peer Prediction for Peer Review: Designing a Marketplace for Ideas

arXiv: https://arxiv.org/abs/2303.16855 · HTML: https://mlsanity.com/paper/2303.16855/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2023.

## Built on by (most cited first, 4 of 4)

- [AgentReview: Exploring Peer Review Dynamics with LLM Agents](https://mlsanity.com/paper/2406.12708/) (2024)
- [Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards](https://mlsanity.com/paper/2505.04966/) (2025)
- [The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review](https://mlsanity.com/paper/2408.13430/) (2024)
- [Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System](https://mlsanity.com/paper/2601.08829/) (2026)

## Abstract (arXiv)

The paper describes a potential platform to facilitate academic peer review with emphasis on early-stage research. This platform aims to make peer review more accurate and timely by rewarding reviewers on the basis of peer prediction algorithms. The algorithm uses a variation of Peer Truth Serum for Crowdsourcing (Radanovic et al., 2016) with human raters competing against a machine learning benchmark. We explain how our approach addresses two large productive inefficiencies in science: mismatch between research questions and publication bias. Better peer review for early research creates additional incentives for sharing it, which simplifies matching ideas to teams and makes negative results and p-hacking more visible.