ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation
Zi Wang, Xingqiao Wang, Sangah Lee, Xiaowei Xu

TL;DR
ARISE is an innovative system that automates scholarly survey paper generation through modular language model agents and iterative rubric-guided refinement, significantly improving quality and adaptability over existing solutions.
Contribution
This paper introduces ARISE, a novel agentic framework employing structured refinement loops and specialized agents for automated, high-quality scholarly survey generation.
Findings
Achieved an average rubric-aligned quality score of 92.48
Outperformed state-of-the-art automated systems and human surveys
Demonstrated improvements in comprehensiveness, accuracy, and formatting
Abstract
The rapid expansion of scholarly literature presents significant challenges in synthesizing comprehensive, high-quality academic surveys. Recent advancements in agentic systems offer considerable promise for automating tasks that traditionally require human expertise, including literature review, synthesis, and iterative refinement. However, existing automated survey-generation solutions often suffer from inadequate quality control, poor formatting, and limited adaptability to iterative feedback, which are core elements intrinsic to scholarly writing. To address these limitations, we introduce ARISE, an Agentic Rubric-guided Iterative Survey Engine designed for automated generation and continuous refinement of academic survey papers. ARISE employs a modular architecture composed of specialized large language model agents, each mirroring distinct scholarly roles such as topic…
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Taxonomy
TopicsTopic Modeling · Expert finding and Q&A systems · Mobile Crowdsensing and Crowdsourcing
