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

翻译：学术文献的快速扩张为生成全面且高质量的学术综述带来了显著挑战。近期智能体系统的进展为自动化传统上依赖人类专业知识的任务（包括文献回顾、综合与迭代优化）提供了重要潜力。然而，现有的自动综述生成方案常存在质量控制不足、格式不规范以及对迭代反馈适应性有限等问题，而这些正是学术写作的核心要素。为应对这些局限，我们提出了ARISE（Agentic Rubric-guided Iterative Survey Engine），一种基于智能体与评价准则引导的迭代式学术综述引擎，专为学术综述论文的自动生成与持续优化而设计。ARISE采用模块化架构，由多个专用大语言模型智能体构成，每个智能体模拟不同的学术角色，如主题拓展、文献引用管理、文献总结、稿件起草以及基于同行评审的评估。ARISE的核心是一个基于评价准则的迭代优化循环，其中多个评审智能体使用结构化、行为锚定的评价准则独立评估稿件草稿，并通过综合反馈系统性地提升内容质量。通过将ARISE与当前最先进的自动系统及近期人工撰写的综述进行对比评估，实验结果表明其性能优越，平均准则对齐质量得分达到92.48。ARISE在全面性、准确性、格式规范及整体学术严谨性等指标上均持续超越基线方法。所有代码、评价准则及生成结果均已公开发布于 https://github.com/ziwang11112/ARISE