ARISE: 基于代理的评分引导迭代调查引擎用于自动化学术论文生成
ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation
AI总结：
ARISE是一种基于代理的评分引导迭代调查引擎，通过模块化架构和评分引导的迭代改进循环，实现高质量学术论文的自动化生成与持续改进。
摘要：
学术文献的快速扩展给综合全面、高质量的学术调查合成带来了重大挑战。最近在代理系统方面的进步为自动化任务提供了很大可能性，这些任务通常需要人类专业知识，包括文献综述、合成和迭代改进。然而，现有的自动调查生成解决方案往往存在质量控制不足、格式差和对迭代反馈适应性差的问题，这些是学术写作的核心要素。为了解决这些限制，我们引入了ARISE，一种基于代理的评分引导迭代调查引擎，用于自动化生成和持续改进学术调查论文。ARISE采用由专门的大语言模型代理组成的模块化架构，每个代理都模仿不同的学术角色，如主题扩展、引文整理、文献总结、论文起草和基于同行评审的评估。ARISE的核心是一个评分引导的迭代改进循环，其中多个评审代理独立使用结构化的、基于行为的评分标准评估草稿，通过合成反馈系统地增强内容。在与最先进的自动化系统和最近的人类撰写调查进行评估时，我们的实验结果表明性能优越，平均评分对齐质量得分为92.48。ARISE在全面性、准确性、格式和整体学术严谨性等指标上均超过基线方法。所有代码、评估评分标准和生成的输出均在https://github.com/ziwang1112/ARISE上公开。
英文摘要：
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 expansion, citation curation, literature summarization, manuscript drafting, and peer-review-based evaluation. Central to ARISE is a rubric-guided iterative refinement loop in which multiple reviewer agents independently assess manuscript drafts using a structured, behaviorally anchored rubric, systematically enhancing the content through synthesized feedback. Evaluating ARISE against state-of-the-art automated systems and recent human-written surveys, our experimental results demonstrate superior performance, achieving an average rubric-aligned quality score of 92.48. ARISE consistently surpasses baseline methods across metrics of comprehensiveness, accuracy, formatting, and overall scholarly rigor. All code, evaluation rubrics, and generated outputs are provided openly at https://github.com/ziwang11112/ARISE
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
