Obtaining high-quality, pre-submission feedback is a critical bottleneck in the academic publication lifecycle for researchers. We introduce AutoRev, an automated author-centric feedback system that generates structured, actionable guidance prior to formal peer review. AutoRev employs a graph-based retrieval-augmented generation framework that models each paper as a hierarchical document graph, integrating textual and structural representations to retrieve salient content efficiently. By leveraging graph-based passage retrieval, AutoRev substantially reduces LLM input context length, leading to higher-quality feedback generation. Experimental results demonstrate that AutoRev significantly outperforms baselines across multiple automatic evaluation metrics, while achieving strong performance in human evaluations. Code will be released upon acceptance.

翻译：获取高质量的预投稿反馈是研究人员在学术出版生命周期中面临的关键瓶颈。本文介绍了AutoRev，一种自动化的作者中心反馈系统，能够在正式同行评审前生成结构化、可操作的指导。AutoRev采用基于图的检索增强生成框架，将每篇论文建模为层次化文档图，整合文本与结构表征以实现高效的关键内容检索。通过利用基于图的段落检索技术，AutoRev显著减少了大型语言模型的输入上下文长度，从而生成更高质量的反馈。实验结果表明，AutoRev在多项自动评估指标上显著优于基线方法，同时在人工评估中展现出强劲性能。代码将在论文录用后开源。