[2603.27360] Defend: Automated Rebuttals for Peer Review with Minimal Author Guidance

Computer Science > Artificial Intelligence arXiv:2603.27360 (cs) [Submitted on 28 Mar 2026] Title:Defend: Automated Rebuttals for Peer Review with Minimal Author Guidance Authors:Jyotsana Khatri, Manasi Patwardhan View a PDF of the paper titled Defend: Automated Rebuttals for Peer Review with Minimal Author Guidance, by Jyotsana Khatri and Manasi Patwardhan View PDF HTML (experimental) Abstract:Rebuttal generation is a critical component of the peer review process for scientific papers, enabling authors to clarify misunderstandings, correct factual inaccuracies, and guide reviewers toward a more accurate evaluation. We observe that Large Language Models (LLMs) often struggle to perform targeted refutation and maintain accurate factual grounding when used directly for rebuttal generation, highlighting the need for structured reasoning and author intervention. To address this, in the paper, we introduce DEFEND an LLM based tool designed to explicitly execute the underlying reasoning process of automated rebuttal generation, while keeping the author-in-the-loop. As opposed to writing the rebuttals from scratch, the author needs to only drive the reasoning process with minimal intervention, leading an efficient approach with minimal effort and less cognitive load. We compare DEFEND against three other paradigms: (i) Direct rebuttal generation using LLM (DRG), (ii) Segment-wise rebuttal generation using LLM (SWRG), and (iii) Sequential approach (SA) of segment-wise rebuttal gen...