APRES: An Agentic Paper Revision and Evaluation System

TL;DR

APRES leverages Large Language Models to automatically revise scientific papers based on an evaluation rubric, improving citation prediction accuracy and human preference, thereby enhancing scientific communication and impact.

Contribution

This paper introduces APRES, a novel LLM-powered system that automatically revises scientific manuscripts to improve their quality and future citation potential without altering core content.

Findings

Improves citation prediction accuracy by 19.6%.

Papers revised by APRES are preferred by experts 79% of the time.

Demonstrates LLMs can effectively stress-test manuscripts before submission.

Abstract

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.