More Than Mimicking Reviewers: Evaluating LLMs for Pre-Submission Peer Review
Read the original on arXiv AI →The paper introduces an author-facing large language model (LLM) system that generates a broad set of atomic concerns about a manuscript and compresses them into a concise report, aiming to provide early peer‑review feedback. Evaluations on 3,398 ICLR 2026 submissions show that the system covers 44.9% of historical reviewer issues on a diagnostic set, rising to 78.7% strict coverage and 84.9% seriousness‑weighted coverage after deduplication and refill, using 3.6× more requests and 5.2× more tokens. Ablation studies reveal that representative selection and matcher sensitivity are key factors limiting the compression quality.
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