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APRES: An Agentic Paper Revision and Evaluation System

arXiv – CS AI|Bingchen Zhao, Jenny Zhang, Chenxi Whitehouse, Minqi Jiang, Michael Shvartsman, Abhishek Charnalia, Despoina Magka, Tatiana Shavrina, Derek Dunfield, Oisin Mac Aodha, Yoram Bachrach|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed APRES, an AI-powered system that uses Large Language Models to automatically revise scientific papers based on evaluation rubrics that predict citation counts. The system improves citation prediction accuracy by 19.6% and produces paper revisions that human experts prefer 79% of the time over original versions.

Key Takeaways

→APRES uses LLMs to automatically revise scientific papers while preserving core scientific content.
→The system discovers evaluation rubrics that are highly predictive of future citation counts.
→Human expert evaluators preferred APRES-revised papers over originals 79% of the time.
→The method improves future citation prediction by 19.6% mean averaged error over baseline approaches.
→The system is designed to augment rather than replace human expert reviewers in the peer review process.

#ai #llm #scientific-research #peer-review #automation #academic-writing #citation-analysis #research-tools

Read Original →via arXiv – CS AI

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