AutoSurvey2自动化生成高质量学术综述，提升文献调研效率。
AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
- 采用多阶段流程，结合检索增强与结构化评估生成综述。
- 在结构连贯性和主题相关性上优于现有自动化方法。
- 适合需要快速撰写领域综述的研究人员使用。
大语言模型等领域的研究文献呈指数增长，人工撰写全面且最新的综述愈发困难。本文提出AutoSurvey2，一种基于检索增强合成与结构化评估的多阶段自动化综述生成框架。系统通过并行章节生成、迭代优化及实时检索最新论文，确保主题覆盖完整与事实准确。质量评估采用多大模型评测框架，从覆盖度、结构和相关性三方面衡量，符合专家评审标准。实验表明，AutoSurvey2在结构连贯性和主题相关性上持续优于现有检索型与自动化基线，同时保持高引用保真度。该框架整合检索、推理与自动评估，提供可扩展、可复现的长篇学术综述生成方案，为未来自动化学术写作研究奠定基础。代码与资源已开源：https://github.com/annihi1ation/auto_research。
原文摘要 · Abstract (English)
The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation. The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy. Quality is assessed using a multi-LLM evaluation framework that measures coverage, structure, and relevance in alignment with expert review standards. Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity. By combining retrieval, reasoning, and automated evaluation into a unified framework, autosurvey2 provides a scalable and reproducible solution for generating long-form academic surveys and contributes a solid foundation for future research on automated scholarly writing. All code and resources are available at https://github.com/annihi1ation/auto_research.
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