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arXiv · 2606.23233

Judgment-Grounded Expansion for Peer Review Generation

Sheng Lu·Lizhen Qu·Iryna Gurevych
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Abstract

Automatic review generation is a promising direction for accelerating scientific progress. While most work adopts an end-to-end setup, its fully automated nature makes it less suitable for settings that demand accountability. To better balance automation and accountability, we formalize judgment-grounded expansion, a human-AI collaboration mode where a reviewer provides an evaluative claim and the system expands it into review comment candidate(s). We model it as a structured generate-check-refine process and conduct a user study to collect human-model interaction data. We study two practical challenges for judgment-grounded expansion: scalable evaluation and candidate set curation. We develop methods to simulate the process for large-scale evaluation, and show that conformal prediction is well suited to balancing candidate set size and target coverage. Our work establishes judgment-grounded expansion as a concrete task and provides empirical and methodological foundations for the design of future collaborative review generation systems.

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BibTeXRIS

Sheng Lu, Lizhen Qu, Iryna Gurevych. 2026-06-22. Judgment-Grounded Expansion for Peer Review Generation. https://arxiv.org/abs/2606.23233

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