Year: 2026
Type: conference-paper
Abstract: 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 retrievalaugmented 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, Au-toRev 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.We have made our code publicly available 1 .
Authors: Maitreya Prafulla Chitale, Ketaki Mangesh Shetye, Harshit Gupta, Manav Chaudhary, Manish Shrivastava, Vasudeva Varma
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415314059