Problem Statement

The current system of academic peer review—long regarded as the gold standard for validating scientific research—is under severe strain. The exponential rise in manuscript submissions, driven in part by AI-assisted writing, is overwhelming a peer review infrastructure that has not scaled accordingly.

The result is a system that is:

• Slow – Manuscripts often face delays of months or even years before publication.
• Expensive – High publication fees exclude underfunded researchers and divert resources from research itself.
• Opaque & inconsistent – Reviews vary widely in quality, and reviewer fatigue or bias undermines trust.
• Vulnerable – The rise of AI-generated “papermill” content and predatory journals threatens the credibility of published science.
• Misaligned – Reviewers are often solicited via impersonal emails, receive little to no recognition or incentive, and must dedicate unpaid hours to a process essential for scientific integrity.

Meanwhile, the complexity and sheer volume of modern research increasingly outpaces the capacity of human reviewers to provide timely, thorough, and equitable assessments.

See Related Nature Article: “The peer-review crisis: how to fix an overloaded system”

Proposed Solution

AI Reviewer is a research and development project that explores how LLMs and agentic AI systems can serve as co-reviewers and assistive agents in the scientific peer review process.

The system aims to:

• Accelerate the dissemination of research by automating routine review tasks.
• Enhance transparency and consistency through structured reasoning and auditability.
• Reduce scholarly publishing costs via reproducible, scalable review pipelines.
• Provide constructive early-stage feedback to authors.
• Improve paper–reviewer matching to optimize expertise alignment.