# From peer review nuances to best practices

arXiv: https://arxiv.org/abs/2607.22681 · HTML: https://mlsanity.com/paper/2607.22681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 20 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [AgentReview: Exploring Peer Review Dynamics with LLM Agents](https://mlsanity.com/paper/2406.12708/) (2024)
- [OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews](https://mlsanity.com/paper/2412.11948/) (2024)
- [NLPeer: A Unified Resource for the Computational Study of Peer Review](https://mlsanity.com/paper/2211.06651/) (2022)
- [Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review](https://mlsanity.com/paper/2412.01708/) (2024)
- [Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis](https://mlsanity.com/paper/2407.12857/) (2024)
- [MOPRD: A multidisciplinary open peer review dataset](https://mlsanity.com/paper/2212.04972/) (2022)
- [ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews](https://mlsanity.com/paper/2503.08506/) (2025)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions](https://mlsanity.com/paper/2505.07920/) (2025)
- [ReviewRL: Towards Automated Scientific Review with RL](https://mlsanity.com/paper/2508.10308/) (2025)
- [ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review](https://mlsanity.com/paper/2510.08867/) (2025)
- [Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework](https://mlsanity.com/paper/2508.21422/) (2025)
- [When AI reviews science: Can we trust the referee?](https://mlsanity.com/paper/2604.23593/) (2026)
- [FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research](https://mlsanity.com/paper/2602.14285/) (2026)

## Abstract (arXiv)

This report studies three nuances in peer review data: paper version, score version, and input format. We characterize how the variants differ, and measure their impact on downstream tasks. Based on our findings, we offer best practices for both data providers and data users.