# Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review

arXiv: https://arxiv.org/abs/2607.22553 · HTML: https://mlsanity.com/paper/2607.22553/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 14 of 14)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [CycleResearcher: Improving Automated Research via Automated Review](https://mlsanity.com/paper/2411.00816/) (2024)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [NLPeer: A Unified Resource for the Computational Study of Peer Review](https://mlsanity.com/paper/2211.06651/) (2022)
- [Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards](https://mlsanity.com/paper/2505.04966/) (2025)
- [ReviewEval: An Evaluation Framework for AI-Generated Reviews](https://mlsanity.com/paper/2502.11736/) (2025)
- [Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://mlsanity.com/paper/2502.17086/) (2025)
- [A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences](https://mlsanity.com/paper/2011.15050/) (2020)
- [Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks](https://mlsanity.com/paper/2506.11113/) (2025)
- [Unveiling the Merits and Defects of LLMs in Automatic Review Generation for Scientific Papers](https://mlsanity.com/paper/2509.19326/) (2025)
- [A System-Level Analysis of Conference Peer Review](https://mlsanity.com/paper/2303.09020/) (2023)

## Built on by (most cited first, 3 of 3)

- [How Closely Do LLM Reviews Align with Human Peer Review?](https://mlsanity.com/paper/2608.03659/) (2026)
- [The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing](https://mlsanity.com/paper/2609.07713/) (2026)
- [A Missing Piece for Trustworthy AI Reviewers: From Benchmarking Rhetorical Robustness to SciCore Review](https://mlsanity.com/paper/2609.39027/) (2026)

## Abstract (arXiv)

Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types of reviewer guidelines, such as official conference guidelines and reviewer-imitating ones generated from high-quality human reviews using LLMs, affect automated peer review. Our experiments show that official conference guidelines produce review results most consistent with human judgments, suggesting that evaluation criteria refined through conference practice serve as effective guidance for automated reviewing as well. In contrast, reviewer-imitating guidelines were generally less effective than official conference guidelines. Furthermore, enforcing strict rubric-style scoring consistently degraded performance, highlighting the importance of allowing subjective and holistic scoring.