APRES: An Agentic Paper Revision and Evaluation System
Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or misunderstood. The primary way scientists communicate their work and receive feedback from the community is through peer review. However, the current system often provides inconsistent feedback between reviewers, ultimately hindering the improvement of a manuscript and limiting its potential impact. In this paper, we introduce a novel method APRES powered by Large Language Models (LLMs) to update a scientific papers text based on an evaluation rubric. Our automated method discovers a rubric that is highly predictive of future citation counts, and integrate it with APRES in an automated system that revises papers to enhance their quality and impact. Crucially, this objective should be met without altering the core scientific content. We demonstrate the success of APRES, which improves future citation prediction by 19.6% in mean averaged error over the next best baseline, and show that our paper revision process yields papers that are preferred over the originals by human expert evaluators 79% of the time. Our findings provide strong empirical support for using LLMs as a tool to help authors stress-test their manuscripts before submission. Ultimately, our work seeks to augment, not replace, the essential role of human expert reviewers, for it should be humans who discern which discoveries truly matter, guiding science toward advancing knowledge and enriching lives.
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Bingchen Zhao
69a7acc32833ff1122ff64e8
Jenny Zhang
69a7acc32833ff1122ff64e9
Chenxi Whitehouse
69a7acc32833ff1122ff64ea
Minqi Jiang
69a7acc32833ff1122ff64eb
Michael Shvartsman
69a7acc32833ff1122ff64ec
Abhishek Charnalia
69a7acc32833ff1122ff64ed
Despoina Magka
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2026-04-13T19:26:26.582Z
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Despoina Magka
marlamagka
user
marlamagka
69a7acc32833ff1122ff64ee
Tatiana Shavrina
69a7acc32833ff1122ff64ef
Derek Dunfield
69a7acc32833ff1122ff64f0
Oisin Mac Aodha
69a7acc32833ff1122ff64f1
Yoram Bachrach
APRES: An Agentic Paper Revision and Evaluation System
Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or misunderstood. The primary way scientists communicate their work and receive feedback from the community is through peer review. However, the current system often provides inconsistent feedback between reviewers, ultimately hindering the improvement of a manuscript and limiting its potential impact. In this paper, we introduce a novel method APRES powered by Large Language Models (LLMs) to update a scientific papers text based on an evaluation rubric. Our automated method discovers a rubric that is highly predictive of future citation counts, and integrate it with APRES in an automated system that revises papers to enhance their quality and impact. Crucially, this objective should be met without altering the core scientific content. We demonstrate the success of APRES, which improves future citation prediction by 19.6% in mean averaged error over the next best baseline, and show that our paper revision process yields papers that are preferred over the originals by human expert evaluators 79% of the time. Our findings provide strong empirical support for using LLMs as a tool to help authors stress-test their manuscripts before submission. Ultimately, our work seeks to augment, not replace, the essential role of human expert reviewers, for it should be humans who discern which discoveries truly matter, guiding science toward advancing knowledge and enriching lives.
Large language models are used to automatically revise scientific papers based on citation-predictive rubrics while preserving core content, achieving improved citation predictions and human evaluator preference.
Large Language Models peer review citation prediction manuscript revision automated writing assistance