AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys

• URL: http://arxiv.org/abs/2510.26012v2
• Date: Sun, 02 Nov 2025 22:15:47 GMT
• Title: AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
• Authors: Siyi Wu, Chiaxin Liang, Ziqian Bi, Leyi Zhao, Tianyang Wang, Junhao Song, Yichao Zhang, Keyu Chen, Xinyuan Song,
• Abstract summary: This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation.<n>The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy.<n> Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines.
• Score: 10.50820843303237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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