PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf
Authors: name: Jiarui Liu, name: Terry Jingchen Zhang, name: Ryan Faulkner, name: X. Angelo Huang, name: Vilém Zouhar, name: Dominik Glandorf, name: Isabel Dahlgren, name: Van Q. Truong, name: Rishit Dagli, name: Yuen Chen, name: Felix Leeb, name: Punya Syon Pandey, name: Yves Bicker, name: Suvajit Majumder, name: Wenyuan Jiang, name: Zeju Qiu, name: Sankalan Pal Chowdhury, name: Bernhard Schölkopf, name: Mona Diab, name: Zhijing Jin
Organization: Jinesis

Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers' writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline uswithout the skill library. We release PaperMentor as open source for public use. Our code is publicly available under the AGPL-3.0 license at https://github.com/jiarui-liu/overleaf