AutoRev: Multi-Modal Graph Retrieval for Automated Peer-Review Generation

• URL: http://arxiv.org/abs/2505.14376v2
• Date: Wed, 08 Oct 2025 11:20:40 GMT
• Title: AutoRev: Multi-Modal Graph Retrieval for Automated Peer-Review Generation
• Authors: Maitreya Prafulla Chitale, Ketaki Mangesh Shetye, Harshit Gupta, Manav Chaudhary, Manish Shrivastava, Vasudeva Varma,
• Abstract summary: AutoRev is an automatic peer-review system designed to provide actionable, high-quality feedback to both reviewers and authors.<n>By modelling documents as graphs, AutoRev effectively retrieves the most pertinent information.<n>We envision AutoRev as a powerful tool to streamline the peer-review workflow, alleviating challenges and enabling scalable, high-quality scholarly publishing.
• Score: 5.72767946092813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enhancing the quality and efficiency of academic publishing is critical for both authors and reviewers, as research papers are central to scholarly communication and a major source of high-quality content on the web. To support this goal, we propose AutoRev, an automatic peer-review system designed to provide actionable, high-quality feedback to both reviewers and authors. AutoRev leverages a novel Multi-Modal Retrieval-Augmented Generation (RAG) framework that combines textual and graphical representations of academic papers. By modelling documents as graphs, AutoRev effectively retrieves the most pertinent information, significantly reducing the input context length for LLMs and thereby enhancing their review generation capabilities. Experimental results show that AutoRev outperforms state-of-the-art baselines by up to 58.72% and demonstrates competitive performance in human evaluations against ground truth reviews. We envision AutoRev as a powerful tool to streamline the peer-review workflow, alleviating challenges and enabling scalable, high-quality scholarly publishing. By guiding both authors and reviewers, AutoRev has the potential to accelerate the dissemination of quality research on the web at a larger scale. Code will be released upon acceptance.

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