@inproceedings{liu-etal-2026-papermentor,
title = "{P}aper{M}entor: A Human-Centered Multi-Agent Writing Tutor for {AI} Research Papers in Overleaf",
author = {Liu, Jiarui and
Zhang, Terry Jingchen and
Faulkner, Ryan and
Huang, Xuanqiang Angelo and
Zouhar, Vil{\'e}m and
Glandorf, Dominik and
Dahlgren, Isabel and
Dagli, Rishit and
Chen, Yuen and
Leeb, Felix and
Truong, Van Q. and
Pandey, Punya Syon and
Bicker, Yves and
Majumder, Suvajit and
Jiang, Wenyuan and
Qiu, Zeju and
Pal Chowdhury, Sankalan and
Sachan, Mrinmaya and
Sch{\"o}lkopf, Bernhard and
Diab, Mona T. and
Jin, Zhijing},
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.39/",
pages = "397--407",
ISBN = "979-8-89176-392-0",
abstract = "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 without the skill library. We release PaperMentor as open source for public use."
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<abstract>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 without the skill library. We release PaperMentor as open source for public use.</abstract>
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%0 Conference Proceedings
%T PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf
%A Liu, Jiarui
%A Zhang, Terry Jingchen
%A Faulkner, Ryan
%A Huang, Xuanqiang Angelo
%A Zouhar, Vilém
%A Glandorf, Dominik
%A Dahlgren, Isabel
%A Dagli, Rishit
%A Chen, Yuen
%A Leeb, Felix
%A Truong, Van Q.
%A Pandey, Punya Syon
%A Bicker, Yves
%A Majumder, Suvajit
%A Jiang, Wenyuan
%A Qiu, Zeju
%A Pal Chowdhury, Sankalan
%A Sachan, Mrinmaya
%A Schölkopf, Bernhard
%A Diab, Mona T.
%A Jin, Zhijing
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F liu-etal-2026-papermentor
%X 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 without the skill library. We release PaperMentor as open source for public use.
%U https://aclanthology.org/2026.acl-demo.39/
%P 397-407
Markdown (Informal)
[PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf](https://aclanthology.org/2026.acl-demo.39/) (Liu et al., ACL 2026)
ACL
- Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, Xuanqiang Angelo Huang, Vilém Zouhar, Dominik Glandorf, Isabel Dahlgren, Rishit Dagli, Yuen Chen, Felix Leeb, Van Q. Truong, Punya Syon Pandey, Yves Bicker, Suvajit Majumder, Wenyuan Jiang, Zeju Qiu, Sankalan Pal Chowdhury, Mrinmaya Sachan, Bernhard Schölkopf, Mona T. Diab, and Zhijing Jin. 2026. PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 397–407, San Diego, California, United States. Association for Computational Linguistics.