More Than Mimicking Reviewers: Evaluating LLMs for Pre-Submission Peer Review

Abstract:Peer-review feedback often arrives too late for authors to make meaningful revisions. We study an author-facing LLM system that moves part of this stress test before submission: it generates a broad pool of atomic concerns and compresses them into a short report. We evaluate agreement with historical reviews and, separately, the possible validity of concerns they omit. From 10,000 ICLR 2026 submissions, we use 3,398 manuscripts with accessible versions that predate review. On a ten-paper diagnostic, independent sampling covers 44.9% of historical issues; deduplication and refill reaches 78.7% strict and 84.9% seriousness-weighted coverage, at 3.6$\times$ more requests and 5.2$\times$ more tokens. A hidden Top-32 Oracle preserves the full 79.3% weighted coverage of a 256-candidate pool, but paper-only selectors retain only 40--44%. LLM review therefore provides broad coverage with a large candidate pool but compresses poorly; ablations identify representative selection and matcher sensitivity as the main sources of this gap.

Artificial Intelligence (cs.AI)

arXiv:2609.05788 [cs.AI]

(or arXiv:2609.05788v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.05788 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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