多模态同行评审模拟：面向社区意识的论文修订可操作待办推荐系统
Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions
- Hong Kong Polytechnic University（香港理工大学）
- Beijing University of Posts and Telecommunications（北京邮电大学）
- Independent Researcher（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一个多模态同行评审模拟系统，通过整合文本和视觉信息，利用检索增强生成技术提升评审质量，并生成可操作的待办列表，以提高论文修订的效率和准确性。
AI中文摘要：
尽管大型语言模型（LLMs）为自动化学术工作流程提供了有前景的能力，但现有的学术同行评审系统仍然受到仅文本输入、有限上下文支撑和缺乏可操作反馈的限制。在本工作中，我们提出了一种交互式基于网页的多模态、社区意识同行评审模拟系统，以在提交论文前有效进行论文修订。我们的框架通过多模态LLMs整合文本和视觉信息，通过基于网络规模OpenReview数据的检索增强生成（RAG）技术提升评审质量，并利用所提出的Action:Objective[#]格式将生成的评审转换为可操作的待办列表，提供结构化和可追溯的指导。该系统无缝集成到现有的学术写作平台中，提供交互式界面用于实时反馈和修订跟踪。实验结果表明，所提出系统在生成更全面和有用的评审方面表现优异，符合专家标准，超越了删减基线，并推进了透明、以人类为中心的学术援助。
英文摘要：
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.
