English(EN) More Than Mimicking Reviewers: Evaluating LLMs for Pre-Submission Peer Review

评估大语言模型用于预提交同行评审以提高反馈及时性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个新的系统，利用大语言模型（LLMs）协助作者进行预提交同行评审。该系统旨在在手稿正式提交前识别潜在问题，以解决反馈来得太晚而无法进行修订的常见问题。通过从大量投稿中生成和压缩各种担忧，大语言模型实现了对历史问题的显著覆盖，尽管压缩仍然是一个挑战。 AI

影响这项研究通过为作者提供更早、更全面的手稿反馈，有望简化学术出版流程。

排序理由该集群包含一篇详细介绍评估大语言模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍评估大语言模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Pouya Parsa, Amin Rezaei · 2026-09-10 04:00

超越模仿审稿人：评估大型语言模型在预提交同行评审中的表现

arXiv:2609.05788v1 Announce Type: new 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 compr…