AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys

📝 Original Info

• Title: AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
• ArXiv ID: 2510.26012
• Date: 2025-10-29
• Authors: ** 제공된 정보에 저자 명단이 포함되어 있지 않아 확인할 수 없습니다. (논문 원문 또는 DOI를 참고하시기 바랍니다.) **

📝 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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