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arxiv: 2406.12708 · v3 · submitted 2024-06-18 · 💻 cs.CL

AgentReview: Exploring Peer Review Dynamics with LLM Agents

Yiqiao Jin , Qinlin Zhao , Yiyang Wang , Hao Chen , Kaijie Zhu , Yijia Xiao , Jindong Wang This is my paper

Pith reviewed 2026-05-23 23:36 UTC · model grok-4.3

classification 💻 cs.CL
keywords peer reviewLLM agentssimulationreviewer biasscientific publishinglatent factorssocial influence theory
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The pith

An LLM agent simulation framework shows reviewer biases cause 37.1% variation in paper decisions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces AgentReview, an LLM-based framework to simulate peer review and separate the effects of multiple hidden factors such as biases. Traditional studies cannot do this cleanly because real review data is private and the contributing factors are hard to isolate. The simulation produces a quantified result: reviewer biases shift paper decisions by 37.1 percent, a pattern consistent with established sociological accounts of social influence, altruism fatigue, and authority bias. This matters for anyone who relies on peer review to decide which research receives attention and resources.

Core claim

AgentReview is the first large language model based peer review simulation framework, which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. The study reveals a notable 37.1% variation in paper decisions due to reviewers' biases, supported by sociological theories such as the social influence theory, altruism fatigue, and authority bias.

What carries the argument

The AgentReview framework, which uses LLM agents to model individual reviewer behaviors and simulate the separate effects of latent factors including biases.

Load-bearing premise

Large language model agents can faithfully reproduce the multivariate biases and decision rules that drive real human reviewers without introducing simulation-specific artifacts.

What would settle it

A direct comparison of decision distributions produced by the AgentReview simulation against decision distributions from a large corpus of actual human peer reviews on identical papers.

Figures

Figures reproduced from arXiv: 2406.12708 by Hao Chen, Jindong Wang, Kaijie Zhu, Qinlin Zhao, Yijia Xiao, Yiqiao Jin, Yiyang Wang.

Figure 1
Figure 1. Figure 1: AGENTREVIEW is an open and flexible framework designed to realistically simulate the peer review process. It enables controlled experiments to disentangle multiple variables in peer review, allowing for an in-depth examination of their effects on review outcomes. Our findings align with established sociological theories. quality and outcomes; 2) Latent Variables. Factors such as reviewer biases and intenti… view at source ↗
Figure 2
Figure 2. Figure 2: Our paper review pipeline consists of 5 phases. Solid [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of initial and final scores with respect to varying number of irresponsible [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of reasons for acceptance and rejections. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Similarities between reviews and meta-reviews w/ various intervention strategies from AC. Left: [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of final decisions with respect to [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of initial and final ratings when varying numbers of reviewers are aware of the [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of final decisions with respect to [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Final rating distribution when we vary one reviewer in the experiment, including their commit [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Characteristics and prompts in AGENTREVIEW. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
read the original abstract

Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exploration and statistics of existing peer review data, which do not adequately address the multivariate nature of the process, account for the latent variables, and are further constrained by privacy concerns due to the sensitive nature of the data. We introduce AgentReview, the first large language model (LLM) based peer review simulation framework, which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. Our study reveals significant insights, including a notable 37.1% variation in paper decisions due to reviewers' biases, supported by sociological theories such as the social influence theory, altruism fatigue, and authority bias. We believe that this study could offer valuable insights to improve the design of peer review mechanisms. Our code is available at https://github.com/Ahren09/AgentReview.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper introduces AgentReview, the first LLM-based peer review simulation framework intended to disentangle the effects of multiple latent factors (including reviewer biases) on peer review outcomes while circumventing privacy constraints of real data. It reports a central quantitative finding of 37.1% variation in paper decisions attributable to biases, interpreted through sociological theories such as social influence theory, altruism fatigue, and authority bias, and releases code for the simulation.

Significance. If the simulation were shown to reproduce human peer-review statistics, the framework could enable controlled study of bias mechanisms and mechanism design without access to sensitive data; the open code is a strength for potential reproducibility. At present the quantitative claims rest on unvalidated agent behavior, limiting immediate applicability.

major comments (3)
  1. [Abstract] Abstract: the headline claim of a 'notable 37.1% variation in paper decisions due to reviewers' biases' is presented without any description of the computation (e.g., how decision variation was aggregated across agent runs, what baseline was subtracted, or whether error bars or sensitivity checks were performed).
  2. [Abstract] Abstract: the assertion that AgentReview 'effectively disentangles' the impacts of latent factors (social influence, altruism fatigue, authority bias) is unsupported by any reported calibration, mapping to real inter-rater agreement statistics, ablation against prompt-only controls, or comparison to observed human bias magnitudes from peer-review datasets.
  3. [Abstract] Abstract: the central modeling assumption that LLM agents can faithfully isolate and replicate the multivariate latent factors driving human reviewers is stated without evidence that the simulation outputs match empirical distributions rather than prompt-induced artifacts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their detailed and constructive comments. We agree that the abstract requires greater transparency regarding the 37.1% figure, the meaning of 'disentangles,' and the modeling assumptions. We have revised the abstract accordingly and provide point-by-point responses below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of a 'notable 37.1% variation in paper decisions due to reviewers' biases' is presented without any description of the computation (e.g., how decision variation was aggregated across agent runs, what baseline was subtracted, or whether error bars or sensitivity checks were performed).

    Authors: We agree the abstract omitted methodological detail. The 37.1% is the mean absolute difference in final accept/reject decisions between bias-enabled and no-bias control simulations, aggregated across 1,000 independent agent runs per paper; a no-bias baseline is subtracted and standard deviations are reported in Section 4. We have added a one-sentence description of this procedure and a reference to the results section in the revised abstract. revision: yes

  2. Referee: [Abstract] Abstract: the assertion that AgentReview 'effectively disentangles' the impacts of latent factors (social influence, altruism fatigue, authority bias) is unsupported by any reported calibration, mapping to real inter-rater agreement statistics, ablation against prompt-only controls, or comparison to observed human bias magnitudes from peer-review datasets.

    Authors: The phrasing 'effectively disentangles' was intended to describe the controlled simulation design that permits independent activation of each factor. We accept that this wording implies stronger validation than is provided. The manuscript contains factor ablations but no direct mapping to human inter-rater statistics, which is precluded by privacy constraints on real review data. We have replaced the phrase with 'simulates the isolated effects of' and added an explicit limitations clause in the abstract. revision: yes

  3. Referee: [Abstract] Abstract: the central modeling assumption that LLM agents can faithfully isolate and replicate the multivariate latent factors driving human reviewers is stated without evidence that the simulation outputs match empirical distributions rather than prompt-induced artifacts.

    Authors: We acknowledge that the abstract presents the modeling assumption without accompanying evidence or caveats. The full paper reports consistency checks and prompt ablations, yet these do not constitute a match to empirical human distributions. We have inserted a brief acknowledgment of the assumption and a pointer to the limitations section discussing potential prompt artifacts. revision: partial

Circularity Check

0 steps flagged

No circularity: simulation outputs treated as independent evidence

full rationale

The paper introduces an LLM-agent simulation framework to explore peer-review dynamics and reports a 37.1% decision variation attributable to biases. This figure is generated by running the forward simulation under different bias conditions rather than by fitting parameters to the simulation's own outputs or by any self-referential definition. No equations, uniqueness theorems, or self-citations are shown that would reduce the reported statistic to a tuned input or to prior work by the same authors. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework relies on the premise that LLM agents can be prompted to exhibit distinct reviewer personas and that varying prompts isolates latent factors without introducing new artifacts. No explicit free parameters or invented entities are named in the abstract, but the simulation design implicitly treats the LLM's response distribution as a faithful proxy for human behavior.

axioms (1)
  • domain assumption LLM agents can be configured to exhibit independent reviewer behaviors that mirror human latent variables such as bias and social influence.
    This premise is required for the simulation to disentangle factors and produce the 37.1% figure; it is invoked when the authors describe the framework as effectively addressing multivariate latent variables.

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Cited by 1 Pith paper

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    AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference sub...

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