arXiv:2606.19749v1 Announce Type: cross Abstract: A new class of agentic review systems are emerging as a remedy to the pressure placed on peer review systems by AI-assisted research, but it is unclear how they should be evaluated. We evaluate two open-source systems (OpenAIReview and coarse), one proprietary system (Reviewer3), and a zero-shot baseline, across six LLMs spanning frontier and efficient models. First, we study whether AI reviews on ICLR/NeurIPS papers track with papers' quality as approximated by external signals such as citations and acceptance decisions. Every system performs
The proliferation of AI-assisted research has created an urgent need for scalable peer review solutions, leading to the development and benchmarking of agentic review systems.
This development signals the automation of critical white-collar workflows in academia and research, paving the way for broader adoption of AI agents in knowledge-intensive industries.
The evaluation and integration of AI-powered peer review systems will accelerate the research publication cycle and fundamentally alter the human role in academic gatekeeping.
- · AI agent developers
- · Research institutions with large publication volumes
- · Scholarly publishers adopting AI tools
- · Developers of foundational LLMs
- · Traditional human peer reviewers
- · Manual academic journal processes
- · Research institutions slow to adopt AI tools
Increased efficiency and throughput in peer review processes.
Potential for new biases or quality control challenges in research evaluation.
Reconfiguration of trust and authority within the scientific community as AI agents become integral to validation.
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