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.
@article{arxiv.2505.14376,
title = {Graph-Guided Passage Retrieval for Author-Centric Structured Feedback},
author = {Maitreya Prafulla Chitale and Ketaki Mangesh Shetye and Harshit Gupta and Manav Chaudhary and Manish Shrivastava and Vasudeva Varma},
journal= {arXiv preprint arXiv:2505.14376},
year = {2026}
}