# Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Models

arXiv: https://arxiv.org/abs/2512.22145 · HTML: https://mlsanity.com/paper/2512.22145/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [PaperBench: Evaluating AI's Ability to Replicate AI Research](https://mlsanity.com/paper/2504.01848/) (2025)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews](https://mlsanity.com/paper/2412.11948/) (2024)
- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [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)
- [Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias](https://mlsanity.com/paper/2405.15739/) (2024)
- [Automatic Evaluation Metrics for Artificially Generated Scientific Research](https://mlsanity.com/paper/2503.05712/) (2025)
- [Power laws in citation distributions: Evidence from Scopus](https://mlsanity.com/paper/1402.3890/) (2014)
- [The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics](https://mlsanity.com/paper/2312.04576/) (2023)

## Built on by (most cited first, 6 of 6)

- [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026)
- [DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review](https://mlsanity.com/paper/2604.09590/) (2026)
- [Preventing the Collapse of Peer Review Requires Verification-First AI](https://mlsanity.com/paper/2601.16909/) (2026)
- [No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions](https://mlsanity.com/paper/2606.13044/) (2026)
- [Allocate Marginal Reviews to Borderline Papers Using LLM Comparative Ranking](https://mlsanity.com/paper/2602.06078/) (2026)
- [IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation](https://mlsanity.com/paper/2610.04074/) (2026)

## Abstract (arXiv)

Large Language Models are versatile general-task solvers, and their capabilities can truly assist people with scholarly peer review as textitpre-review agents, if not as fully autonomous textitpeer-review agents. While incredibly beneficial, automating academic peer-review, as a concept, raises concerns surrounding safety, research integrity, and the validity of the academic peer-review process. The majority of the studies performing a systematic evaluation of frontier LLMs generating reviews across science disciplines miss the mark on addressing the alignment/misalignment of reviews along with the utility of LLM generated reviews when compared against publication outcomes such as Citations, Hit-papers, Novelty, and Disruption. This paper presents an experimental study in which we gathered ground-truth reviewer ratings from OpenReview and used various frontier open-weight LLMs to generate reviews of papers to gauge the safety and reliability of incorporating LLMs into the scientific review pipeline. Our findings demonstrate the utility of frontier open-weight LLMs as pre-review screening agents despite highlighting fundamental misalignment risks when deployed as autonomous reviewers. Our results show that all models exhibit weak correlation with human peer reviewers (0.15), with systematic overestimation bias of 3-5 points and uniformly high confidence scores (8.0-9.0/10) despite prediction errors. However, we also observed that LLM reviews correlate more strongly with post-publication metrics than with human scores, suggesting potential utility as pre-review screening tools. Our findings highlight the potential and address the pitfalls of automating peer reviews with language models. We open-sourced our dataset DLMRSD to help the research community expand the safety framework of automating scientific reviews.