AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
This provides a scalable and reproducible solution for researchers struggling with the increasing volume of literature, though it is incremental as it builds on existing automated methods.
The paper tackles the challenge of generating comprehensive and current survey papers amid rapid research growth, particularly in large language models, by introducing AutoSurvey2, a multi-stage pipeline that automates survey generation. Experimental results show it outperforms existing baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity.
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.