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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||2 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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