用多模态AI模拟审稿，自动生成可执行修改清单。
Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions
- 融合文本与图像的多模态大模型生成审稿意见。
- 基于开放审稿数据增强反馈，生成更全面的评审结果。
- 输出可追踪的待办任务清单，适合预投稿修订使用。
尽管大语言模型在自动化学术流程方面展现潜力，但现有同行评审系统仍受限于纯文本输入、上下文支撑不足以及缺乏可操作反馈。本文提出一个交互式网页系统，支持多模态、社区感知的同行评审模拟，帮助作者在投稿前有效修改稿件。该框架通过多模态大模型整合文本与视觉信息，利用基于网络规模OpenReview数据的检索增强生成（RAG）提升评审质量，并将生成的评审意见转化为可执行的待办清单（采用Action:Objective[#]格式），提供结构化且可追溯的指导。系统可无缝集成至现有学术写作平台，支持实时反馈与修订追踪。实验表明，该系统生成的评审意见比消融基线更全面、更符合专家标准，推动了透明、以人为本的学术辅助发展。
原文摘要 · Abstract (English)
While large language models (LLMs) offer promising capabilities for automating academic workflows, existing systems for academic peer review remain constrained by text-only inputs, limited contextual grounding, and a lack of actionable feedback. In this work, we present an interactive web-based system for multimodal, community-aware peer review simulation to enable effective manuscript revisions before paper submission. Our framework integrates textual and visual information through multimodal LLMs, enhances review quality via retrieval-augmented generation (RAG) grounded in web-scale OpenReview data, and converts generated reviews into actionable to-do lists using the proposed Action:Objective[\#] format, providing structured and traceable guidance. The system integrates seamlessly into existing academic writing platforms, providing interactive interfaces for real-time feedback and revision tracking. Experimental results highlight the effectiveness of the proposed system in generating more comprehensive and useful reviews aligned with expert standards, surpassing ablated baselines and advancing transparent, human-centered scholarly assistance.
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