AI-Assisted Peer Review Systems

• AI-Assisted Peer Review Systems are innovative frameworks that use LLM-powered agents to simulate and streamline the review process while addressing bias and workload issues.
• They integrate modular designs like AgentReview, PeerArg, and ReviewerToo, employing quantitative simulations and argumentation pipelines to improve decision accuracy and explainability.
• These systems leverage synthetic data and real-time monitoring techniques to ensure privacy, implement dynamic bias audits, and enhance overall fairness in scholarly evaluation.

Artificial intelligence–assisted peer review systems (“AI-assisted peer review”) comprise a diverse and rapidly evolving array of methods, platforms, and workflow augmentations that leverage LLMs and related technologies to analyze, generate, or synthesize peer reviews of academic manuscripts. These systems aim to address structural problems of contemporary peer review—including scale, consistency, fairness, and workload—by integrating advanced machine learning into one or more stages of the evaluation pipeline. Modern AI-assisted peer review workflows encompass agent-based simulation, decision-support and critique synthesis, content-focused bias analysis, and multi-agent deliberation, raising both technical and sociological research questions.

1. Framework Architectures and System Designs

Distinct architectures for AI-assisted peer review have emerged, ranging from agent-based LLM simulations to pipeline integrations within live conference/journal workflows.

AgentReview models the entire peer review process with LLM-powered agents assigned to the roles of reviewers, authors, and area chairs (ACs). The review is orchestrated across five phases: (1) reviewer assessment, (2) author–reviewer rebuttal, (3) reviewer–AC post-rebuttal discussion, (4) meta-review compilation, and (5) final decision, with an overall acceptance rate controlled at 32%. Each agent (GPT-4, “gpt-4-1106-preview”) receives a fixed persona, governed entirely by prompt engineering and covering axes of expertise, commitment, intention, and AC style—without parameter-level fine-tuning. The orchestration layer (“director”) manages prompt construction and data flow, ensuring all synthetic outputs (∼53,800) are logged while maintaining strict privacy, as no real peer-review data or human reviewer identities are used (Jin et al., 18 Jun 2024).

PeerArg introduces a hybrid LLM + formal argumentation pipeline, decomposing the evaluation into extraction of quantitative bipolar argumentation frameworks (QBAF) from review texts, per-aspect sentiment assessment, and structured symbolic aggregation for decision prediction. The pipeline processes free-text reviews into aspect-specific arguments (e.g., novelty, clarity, impact), applies graph-based argumentation semantics, and aggregates into a final accept/reject decision, enhancing explainability over black-box aggregation (Sukpanichnant et al., 25 Sep 2024).

ReviewerToo enables modular experimental and deployment configurations by decomposing the workflow into specialized agent modules: literature reviewer (conducts external retrieval), persona-configurable reviewer agents, an author agent for rebuttal, and a meta-reviewer module for multi-agent aggregation and fact-checking. Its design supports variation in persona diversity, rubric weighting, and review synthesis, reflecting state-of-the-art practices for integrating LLMs or human experts at any pipeline stage (Sahu et al., 9 Oct 2025).

2. Simulation Methodologies and Quantification of Latent Effects

Rigorous experimental setups lie at the core of AI-assisted peer review research, enabling the disentangling and measurement of latent process variables.

AgentReview uses a large-scale simulation (523 ICLR submissions, stratified by tier) with three LLM reviewers per paper drawn from a diverse persona space. Key manipulated variables include reviewer commitment, intention, and knowledge; AC decision style; author anonymity; and the presence/absence of rebuttal or numeric rating. Output datasets—comprising tens of thousands of reviews, rebuttals, discussions, and decisions—enable statistical quantification of key drivers:

• Reviewer bias: induces a $\Delta_{\text{bias}} \approx 0.371$ variation in decisions, calculated as the mean absolute difference in binary accept/reject outcomes between biased and unbiased reviewer assignments.
• Authority bias: revealing author identities for only 10% of papers causes a 27.7% change in decision outcomes.
• Social influence: cross-reviewer rating standard deviation drops by 27.2% through scripted post-review discussion, modeled by

$$s_i^{(t+1)} = \alpha s_i^{(t)} + (1-\alpha)\frac{1}{|\mathcal N(i)|} \sum_{j\in \mathcal N(i)} s_j^{(t)}$$

where $s_i^{(t)}$ is the score of reviewer $i$ at time $t$, and $\mathcal N(i)$ is the neighborhood of peer reviewers (Jin et al., 18 Jun 2024).

ReviewerToo validates its multi-agent design against a stratified, real-world ICLR 2025 dataset (1,963 papers) with official decisions. It reports overall meta-level LLM committee accuracy of 81.8% for binary accept/reject, vs. 83.9% for the average human reviewer. Review text quality is benchmarked through LLM-judge ELO ratings, showing that meta-reviewer output surpasses the average human while still trailing the top 1% of human reviewers (Sahu et al., 9 Oct 2025).

3. Privacy-Preserving Data Generation and Ethical Considerations

AI-assisted peer review systems raise profound privacy, data protection, and ethical questions.

AgentReview enforces “privacy by design” through exclusive reliance on machine-generated data, never ingesting real reviews or confidential discussion, and thereby bypassing IRB and consent constraints. Privacy is guaranteed via (i) exclusion of any human-annotated peer-review data, (ii) complete de-identification of input data (public manuscripts only), and (iii) synthetic simulation of all inter-agent interactions. No differential privacy mechanism is invoked, but risk is mitigated by not touching sensitive artifacts (Jin et al., 18 Jun 2024).

Broader system recommendations emphasize the need for:

• Synthetic datasets in scenario simulation to preempt live privacy hazards.
• Editorial sandboxes and internal pilot deployments to stress-test bias without exposing real submissions.
• Limitations of synthetic agents in fully capturing the tacit expertise and cultural context embedded in real peer-review.
• Explicit audit logs and versioning to support downstream accountability (Jin et al., 18 Jun 2024, Sahu et al., 9 Oct 2025, Wei et al., 9 Jun 2025).

4. Quantitative Findings and Sociological Mechanism Insights

Empirical experiments with AI-assisted systems have revealed both the magnitude and the social mechanisms underpinning decision variation in peer review.

Key findings from AgentReview simulations include:

• 37.1% of final decisions are attributable to reviewer-level biases.
• The presence of a single irresponsible reviewer reduces group review effort by 18.7% (measured in review word count).
• Rebuttal phases affect outcome probability negligibly, indicating a strong anchoring effect that resists update through author argumentation.
• Social influence reduces the diversity of reviewer opinion, confirming sociological theory predictions.

Sociological constructs affirmed include:

• Social influence theory: rating convergence in collective discussions.
• Altruism fatigue: decline in motivation when surrounded by uncommitted reviewers.
• Authority bias: decisions shift in favor of submissions from prestigious or known authors.
• Anchoring: initial ratings are rarely overturned by rebuttal, limiting the corrective impact of the author response phase.
• Groupthink and echo chamber effects: clusters of malicious reviewers exaggerate negative consensus (Jin et al., 18 Jun 2024).

5. Bias Detection, Mitigation, and System Design Recommendations

The design of next-generation AI-assisted peer review systems is grounded in quantitative evidence regarding process failure modes and leverage points for improvement.

Specific recommendations from AgentReview include:

• Dynamic bias audits: run agent-based simulations to explore policy changes such as new assignment mechanisms or rating calibrations before real-world rollout.
• Enhanced double-blind protocols: randomized and stricter enforcement of author anonymity to reduce authority bias impact.
• Real-time reviewer-commitment monitoring: proactively flagging and potentially replacing underperforming reviewers to prevent negative peer effects.
• Adaptive rebuttal structuring: prompt reviewers to explicitly reevaluate initial critiques to overcome anchoring (Jin et al., 18 Jun 2024).
• Integration proposals involve dedicated editorial sandboxes for system “what-if” testing, transparent policy development, and cost-effective simulation of rare or adversarial scenarios.

ReviewerToo highlights the added value of ensemble and multi-persona LLM reviewers for consistency and actionable feedback, recommends human-AI hybrid designs for optimal fairness, and stresses human final responsibility for nuanced theoretical and methodological judgements (Sahu et al., 9 Oct 2025).

6. Limitations, Generalizability, and Future Research Frontiers

While agent-driven and hybrid AI peer review systems deliver substantial insights, several systemic limitations and research questions persist:

• Synthetic LLM agents and simulated workflows cannot exhaustively encode the complete subtleties of human reviewing communities and domain-specific expertise.
• Evaluation is often limited to computer science conference data, raising questions of generalizability to other scientific domains.
• Computational cost and scalability must be weighed against practical system benefits for large-scale deployment.
• Risks of over-reliance on synthetic review signals and the challenge of overfitting to simulation artifacts remain.
• Advanced adversarial techniques, new forms of dynamic reviewer modeling, and sociotechnical governance for AI-assisted decision making constitute open research fronts (Jin et al., 18 Jun 2024, Sahu et al., 9 Oct 2025, Wei et al., 9 Jun 2025).

Ongoing areas of investigation include adversarial and stress testing of peer review protocols using agent simulations, domain-specific adaptation (e.g., to biomedical or social sciences), and meta-scientific studies quantifying how AI interventions affect long-term fairness, reliability, and equity in scientific publication.

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References:

• AgentReview: Exploring Peer Review Dynamics with LLM Agents (Jin et al., 18 Jun 2024)
• PeerArg: Argumentative Peer Review with LLMs (Sukpanichnant et al., 25 Sep 2024)
• ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review (Sahu et al., 9 Oct 2025)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning (Wei et al., 9 Jun 2025)